A method and system for measuring a coordinated expansion mode of construction land and a traffic network

Abstract

The application provides a kind of construction land and traffic network's coordinated expansion mode measurement method and system, including obtaining the construction land, traffic network and population multi-source data of the same period identification in research area and carrying out spatial consistent processing, constructs the structured data system of space-time matching;Based on the system, the population density and construction land density of each space unit are calculated, and the multi-class urban-rural gradient zone is divided by means of clustering algorithm;Urban-rural gradient zone is used as the basis for analysis unit, and the construction land growth rate and road growth rate of different time periods are accurately calculated, and the construction and traffic ratio index is further calculated, and finally the ratio index and road growth rate are combined to complete the classification and distribution result generation of coordinated expansion mode, realize the multi-dimensional, dynamic and accurate characterization of the coordinated relationship between urban construction and traffic network, reveal the evolution law of urban spatial structure, and provide scientific support for urban spatial planning, infrastructure layout optimization and regional sustainable development.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban geographic space analysis and land use research, and particularly relates to a construction land and traffic network coordinated expansion mode measurement method and system. BACKGROUND

[0002] Urban construction and traffic network are core elements of urban space development, and their coordination directly affects urban operation efficiency, land use rationality and regional sustainable development level. With the acceleration of urbanization process, there are obvious spatial differences and non-uniformity in construction land expansion and traffic infrastructure development in China. On the one hand, the construction land in some urban areas grows rapidly, and the traffic network lags behind, forming travel bottlenecks, road congestion, increasing pressure on public transportation and low efficiency of land use, which further affects the quality of life of urban residents and the sustainable operation of urban functional areas. On the other hand, over-laying or one-way expansion of traffic network may also lead to uncoordinated urban expansion, resulting in waste of land resources and increased pressure on the ecological environment.

[0003] Existing research on the relationship between urban construction and traffic network mainly focuses on single-dimensional analysis, which is difficult to fully depict the coordinated evolution relationship between the two. In addition, previous research often ignores the urban-rural gradient difference and the dynamic matching characteristics of construction and traffic expansion in space and time, lacks systematic investigation of the dynamic matching characteristics of urban construction and traffic network in space and time, and thus has insufficient ability to identify and warn of problems such as unbalanced development of urban functional areas, traffic lag or rapid construction. Especially in cross-regional or cross-period comparison, there is a lack of unified quantitative indicators and analysis framework, making it difficult to effectively compare and evaluate the construction-traffic coordination of different cities or regions.

[0004] Therefore, it is urgent to propose a quantitative measurement method that can comprehensively consider the expansion of construction land and the evolution of traffic network under different urban-rural gradients, dynamically identify the coordinated expansion mode of the two, and reveal the evolution law of urban spatial structure, so as to provide scientific support for urban spatial planning, infrastructure layout optimization and regional sustainable development. SUMMARY

[0005] The present application provides a construction land and traffic network coordinated expansion mode measurement method and system to realize dynamic quantitative measurement of the coordinated expansion mode of construction land and traffic network, effectively consider the urban-rural gradient difference, and provide scientific decision-making basis for urban spatial planning, infrastructure layout optimization and regional sustainable development.

[0006] In one aspect, the present application provides a construction land and traffic network coordinated expansion mode measurement method, which comprises:

[0007] acquire construction land data, traffic network data and population data with the same time period identifier in a research area, and perform spatial consistency processing to establish spatial correlation of the construction land data, the traffic network data and the population data in different spatial units, and obtain structured data consistent in time and space;

[0008] based on the structured data, calculate population density and construction land density of each spatial unit in the research area;

[0009] based on the population density and the construction land density, divide the research area into a plurality of urban-rural gradient zones using a clustering algorithm, and assign attribute labels to each urban-rural gradient zone; the attribute labels include population density, construction land proportion, road density and road grade;

[0010] take the urban-rural gradient zone as a basic analysis unit, calculate construction land growth rate and road growth rate of each urban-rural gradient zone in multiple time periods;

[0011] based on the construction land growth rate and the road growth rate, calculate a ratio index of construction and traffic in each urban-rural gradient zone;

[0012] based on the ratio index and the road growth rate, classify each urban-rural gradient zone into a cooperative expansion mode, and generate a mode distribution result.

[0013] In another aspect, the present application also provides a system for measuring cooperative expansion mode of construction land and traffic network, which comprises:

[0014] an acquisition module for acquiring construction land data, traffic network data and population data with the same time period identifier in a research area, and performing spatial consistency processing to establish spatial correlation of the construction land data, the traffic network data and the population data in different spatial units, and obtaining structured data consistent in time and space;

[0015] a first calculation module for calculating population density and construction land density of each spatial unit in the research area based on the structured data;

[0016] a division module for dividing the research area into a plurality of urban-rural gradient zones using a clustering algorithm based on the population density and the construction land density, and assigning attribute labels to each urban-rural gradient zone; the attribute labels include population density, construction land proportion, road density and road grade;

[0017] a second calculation module for taking the urban-rural gradient zone as a basic analysis unit, and calculating construction land growth rate and road growth rate of each urban-rural gradient zone in multiple time periods;

[0018] A third calculation module is configured to calculate a construction-traffic ratio index in each urban-rural gradient zone based on the construction land growth rate and the road growth rate;

[0019] A generation module is configured to classify each urban-rural gradient zone into a coordinated expansion mode based on the ratio index and the road growth rate, and generate a mode distribution result.

[0020] The construction land and traffic network coordinated expansion mode measurement method and system provided by the application can achieve multi-dimensional, dynamic and accurate characterization of the coordinated relationship between urban construction and traffic networks, reveal the evolution law of urban spatial structure, and provide scientific support for urban spatial planning, infrastructure layout optimization and regional sustainable development. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0022] Figure 1 is a flowchart of the construction land and traffic network coordinated expansion mode measurement method provided by the embodiments of the application;

[0023] Figure 2 is a schematic diagram of the multiple urban-rural gradient zones established;

[0024] Figure 3 is a coordinate diagram for identifying the coordinated expansion mode;

[0025] Figure 4 is a multi-period urban-rural gradient division result diagram;

[0026] Figure 5 is a construction land expansion analysis result diagram;

[0027] Figure 6 is a different expansion mode proportion result diagram;

[0028] Figure 7 is a structural schematic diagram of a construction land and traffic network coordinated expansion mode measurement system provided by an embodiment of the present application;

[0029] Figure 8 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described below in detail with reference to the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0031] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second" and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0032] Figure 1 is a flowchart of a construction land and traffic network coordinated expansion mode measurement method provided by an embodiment of the present application.

[0033] As shown in Figure 1 , the construction land and traffic network coordinated expansion mode measurement method provided by an embodiment of the present application mainly includes the following steps:

[0034] 101, construction land data, traffic network data and population data with the same time period identifier in a research area are acquired, and spatial consistency processing is performed, spatial correlation relationships of the construction land data, the traffic network data and the population data in different spatial units are established, and spatially and temporally consistent structured data is obtained;

[0035] In a specific implementation process, the construction land data, the traffic network data and the population grid data of the research area can be extracted from the existing multi-source spatial database, and it is ensured that the data covers the same time period. The construction land data can be derived from a land cover or land use data set with high spatial resolution, which can reflect the distribution characteristics of built-up areas in different periods. The traffic network data can adopt spatial vector data including road level, node distribution and connectivity attribute information.

[0036] The construction land data, the traffic network data and the population data can be spatially matched and boundary cropped according to a unified spatial reference system and resolution, so that the construction land data, the traffic network data and the population data are consistent in spatial range and coordinate system, and a spatial correlation between the construction land data, the traffic network data and the population data is established through spatial overlay analysis, to obtain structured data consistent in space and time.

[0037] Specifically, the data at different times can be uniformly processed in coordinate projection to be converted to the same spatial reference system, so as to ensure consistent spatial position accuracy. The road data is uniformly processed into a vector line format, and road grade coding is standardized. The construction land data, the traffic network data and the population data of the study area are extracted and spatially overlaid to establish a spatial correspondence between the three types of data. For each construction land unit, the traffic network attributes and the population quantity covered thereby are extracted, to realize unitized matching of the population, the road and the construction land, and to provide structured data support for subsequent urban-rural gradient division.

[0038] 102. Based on the structured data, population density and construction land density of each spatial unit in the study area are calculated;

[0039] In a specific implementation process, the population density (person / square kilometer) and the construction land density (construction land area proportion) of each grid unit in the study area can be calculated based on the previously obtained structured data consistent in space and time, in combination with the global settlement grading idea and the actual characteristics of the region.

[0040] 103. Based on the population density and the construction land density, a clustering algorithm is used to divide the study area into a plurality of urban-rural gradient zones, and each urban-rural gradient zone is given an attribute label; the attribute label includes population density, construction land proportion, road density and road grade;

[0041] In a specific implementation process, the division of urban-rural gradient zones based on population density and construction land density can be realized through various clustering algorithms. For example, in addition to the commonly used k-means algorithm, a hierarchical clustering algorithm or a density clustering algorithm can also be used, and a suitable algorithm is selected according to the data distribution characteristics to realize more accurate grading. The divided urban-rural gradient zones can reflect the continuous change characteristics from the urban center to the rural area.

[0042] Specifically, a clustering algorithm can be used to comprehensively classify the population density and construction land density to form a continuous distribution sequence from high urbanization to non-urbanization; the research area is divided into multiple urban-rural gradient zones according to the comprehensive classification results, and a spatial vector boundary is generated, so that the urban-rural gradient zones seamlessly cover and do not overlap each other; wherein the urban-rural gradient zones include at least two of the city center area, the dense construction area, the semi-dense area, the suburban transition area, the rural settlement area, the scattered rural area and the uninhabited area. Wherein, Figure 2 is a schematic diagram of the multiple urban-rural gradient zones established, as Figure 2 shown, the urban-rural gradient zones show that the population gradually decreases from the suburban transition area to the uninhabited area, and gradually increases to the city center area. When the city expands, the rural area shrinks, the uninhabited area presents rural densification to the suburban transition area, and the suburban transition area presents urban densification to the city center area.

[0043] Wherein, the comprehensive classification result refers to the classification label output based on the clustering algorithm, and the urban-rural gradient division system generated in combination with the actual geographical features. It can be realized by setting a reasonable classification threshold or using the natural breakpoint method, with the purpose of ensuring the scientificity and practicality of the urban-rural gradient zone division. The spatial vector boundary refers to the geometric figure used to describe the spatial range of the urban-rural gradient zone, which can be generated by the vectorization tool in the GIS software, with the purpose of eliminating the gaps or overlapping phenomena between the urban-rural gradient zones, so as to ensure the integrity and exclusivity of the spatial unit.

[0044] In detail, it can be realized in the following way:

[0045] (1) Comprehensive classification of population density and construction land density, using clustering algorithm (k-means) to divide the indicators into several levels, so that the high urbanization area and the low-density rural area are continuously mapped in space.

[0046] (2) Denoising processing, including deleting road segments and isolated pixels with a length less than a certain threshold (such as 50m), and eliminating remote sensing data misjudgment or road data noise.

[0047] (3) According to the comprehensive classification results, the research area is divided into seven urban-rural gradient zones: city center area, dense construction area, semi-dense area, suburban transition area, rural settlement area, scattered rural area and uninhabited area. Using the spatial vector boundary generation tool, a spatial layer is created for each urban-rural gradient zone to ensure that the urban-rural gradient zones are continuously covered in space and do not overlap.

[0048] (4) Assign a complete attribute label to each urban-rural gradient zone, including: population density, construction land proportion, land use type, road density, road grade, and road length, etc. Through spatial overlay and attribute association, an urban-rural gradient attribute database is formed, which provides a data basis for subsequent calculation of construction land growth rate, road growth rate, and identification of construction-traffic coordinated expansion mode.

[0049] 104. Calculate the construction land growth rate and road growth rate of each urban-rural gradient zone at multiple time periods, taking the urban-rural gradient zone as the basic analysis unit.

[0050] In a specific implementation process, the construction land area and total road length in each period can be extracted; the ratio of the construction land area in the adjacent period to the construction land area in the initial period is calculated as the construction land growth rate; the ratio of the total road length in the adjacent period to the total road length in the initial period is calculated as the road growth rate; the construction land growth rate and road growth rate of each urban-rural gradient zone are arranged in chronological order to form a time series matrix.

[0051] In practical applications, the time series matrix can be understood as a structured data framework that integrates time dimension information and retains the continuous evolution trajectory of the expansion process. This matrix form provides a basis for analyzing the time series fluctuation characteristics and trend of the growth rate, and also supports the dynamic recognition ability of subsequent ratio index calculation and coordinated mode classification.

[0052] In detail, this step can be implemented in the following way:

[0053] (11) Delete construction land pixels with an area less than a certain threshold (such as 100 m²) and road segments with a length less than a threshold (such as 50 m) to eliminate remote sensing data misjudgment and road data anomalies.

[0054] (12) Take the constructed multi-level urban-rural gradient zone as the basic analysis unit, and extract the total construction land area and total road length of each single urban-rural gradient zone at each study period.

[0055] (13) For each urban-rural gradient zone, calculate the construction land growth rate in the adjacent period:

[0056]

[0057] wherein A(t1, t2, i) represents the construction land area in the i-th zone within the time interval T from the initial year t1 to the final year t2.

[0058] (14) Calculate the road growth rate:

[0059] ​​

[0060] wherein, and denotes the total length of the road network in the ith zone within the time interval T, from the initial year t1 to the final year t2.

[0061] (15) arranging the construction land growth rate and the road growth rate of each urban-rural gradient zone in time sequence to form a time series matrix for analyzing the expansion trend and fluctuation characteristics in the urban-rural gradient zone.

[0062] The above scheme solves the problem of insufficient comparability of expansion characteristics between different urban-rural gradient zones caused by ignoring the initial size difference by clearly defining the calculation method of the growth rate, thereby improving the accuracy of subsequent collaborative expansion mode recognition. By introducing the relative change principle and the time series matrix, the scheme not only realizes the accurate quantification of the dynamic expansion of construction and transportation, but also provides scientific support for revealing the evolution law of urban spatial structure.

[0063] 105. Based on the construction land growth rate and the road growth rate, calculate the ratio index of construction and transportation in each urban-rural gradient zone;

[0064] In one specific implementation process, the ratio of the construction land growth rate and the road growth rate can be taken as the ratio index; and the ratio index is associated with the urban-rural gradient zone, and the ratio index of different urban-rural gradient zones in different time periods is statistically analyzed and visualized; the ratio index of each urban-rural gradient zone and the time sequence variation characteristics of the ratio index are output to form a construction and transportation collaboration degree index database, providing a quantitative basis for subsequent collaborative expansion mode identification.

[0065] 106. Based on the ratio index and the road growth rate, classify the collaborative expansion mode of each urban-rural gradient zone to generate a mode distribution result.

[0066] In a specific implementation process, the relative rate relationship of construction expansion and traffic evolution can be represented by a ratio index, and the absolute direction of traffic system change can be represented by a road growth rate; a ratio index threshold interval is set, and the ratio index threshold interval is dynamically adjusted according to the distribution characteristics of the construction land and the traffic network expansion rate, a two-dimensional discrimination framework is constructed; the corresponding absolute direction of the ratio index threshold interval and the road growth rate is used for classification of the coordinated expansion mode, and a mode distribution result is generated. When the road growth rate is greater than zero and the ratio index is greater than the upper limit value (such as 1.1) of the ratio index threshold interval, it is classified as a construction leading type, that is, the construction expansion is significantly faster than the traffic growth, and the traffic support is insufficient; when the road growth rate is greater than zero and the ratio index is between the lower limit value (such as 0.9) of the ratio index threshold interval and the upper limit value of the ratio index threshold interval, it is classified as a coordinated expansion type, that is, the construction and traffic grow synchronously, and develop coordinately; when the road growth rate is greater than zero and the ratio index is less than the lower limit value of the ratio index threshold interval, it is classified as a traffic leading type, that is, the traffic expansion leads the construction land, and the traffic guides the development. The proportion and distribution characteristics of different coordinated expansion modes in space can be calculated by counting each coordinated expansion mode in the same urban-rural gradient zone. A spatial distribution map of the coordinated expansion mode of each urban-rural gradient zone is generated, and different coordinated expansion modes are distinguished by color and / or symbol. Figure 3 is a coordinate diagram for identifying the coordinated expansion mode, the horizontal coordinate is the road growth rate , and the vertical coordinate is the construction land growth rate , and the ratio index is represented by BTRI.

[0067] Specifically, firstly, the relative rate relationship of construction expansion and traffic evolution and the absolute direction of traffic system change are captured respectively by the two-dimensional representation of the ratio index and the road growth rate, avoiding the confusion problem caused by a single index. Secondly, when setting the ratio index threshold interval, the threshold range can be dynamically adjusted according to the distribution characteristics of the actual expansion rate, rather than using a fixed threshold, which makes the threshold adaptively change with the rate distribution characteristics of different urban-rural gradient zones, thereby effectively overcoming the problem of lack of universality of the threshold caused by the difference between urban and rural gradients. Finally, by combining the dynamically adjusted ratio index threshold interval and the corresponding absolute direction of the road growth rate, a two-dimensional discrimination framework is formed, ensuring that the identification of the construction leading type, the coordinated expansion type or the traffic leading type strictly matches the actual expansion state of each urban-rural gradient zone, thereby generating a mode distribution result that accurately reflects the spatio-temporal evolution law. This process not only solves the systematic deviation problem of the fixed threshold when applied across urban-rural gradient zones, but also improves the scientificity and practicality of the classification result by combining the expansion rate distribution characteristics.

[0068] Further, the ratio index threshold interval can be dynamically adjusted according to the distribution characteristics of the construction land and the traffic network expansion rate, which can include the following ways:

[0069] a. Grouping according to the urban-rural gradient zone type, extracting the construction land expansion rate and road expansion rate of all time periods in each group respectively, forming the sample set of each group, calculating the mean, standard deviation and skewness coefficient of each sample set, and obtaining the distribution difference characteristics of the expansion rate of different urban-rural gradient zones;

[0070] Specifically, first, the research area is grouped according to the urban-rural gradient zone (such as urban center, dense construction area, etc.). For each urban-rural gradient zone, the construction land expansion rate and road expansion rate data of all time periods (such as 2000-2005, 2005-2010, etc.) are extracted to form a sample set. Then, the mean (reflecting the average expansion level), standard deviation (reflecting the dispersion degree of the expansion rate) and skewness coefficient (reflecting the asymmetry of the distribution) of each sample set are calculated. Through these statistical quantities, the distribution difference of the expansion rate in different urban-rural gradient zones can be quantified, for example, the urban center may present high mean and low standard deviation, while the rural area may present low mean and high standard deviation, thereby providing data basis for threshold adjustment.

[0071] b. The change slope of the expansion rate in the continuous time period of each group sample is fitted by a linear regression model to obtain the time trend coefficient, and the increasing, decreasing or stable trend of the expansion rate is identified according to the time trend coefficient to determine the time adaptation direction of the threshold adjustment, and a time dimension adaptation parameter set is formed;

[0072] Specifically, for each urban-rural gradient zone, a linear regression model is used to fit each expansion rate with the time variable (such as year) to obtain the regression slope (i.e. time trend coefficient). A positive slope indicates an increasing expansion rate, a negative slope indicates a decreasing expansion rate, and a slope close to zero indicates stability. This trend information is used to guide the direction of threshold adjustment, for example, if the expansion rate continues to increase, the threshold interval may need to be relaxed to adapt to high-speed expansion; if it decreases, the threshold may need to be tightened.

[0073] c. The land use efficiency and population growth elasticity coefficient are used as auxiliary verification indexes, when the fitting degree of the auxiliary indexes and the expansion rate distribution characteristics is lower than the preset fitting degree, the sample set is subjected to outlier elimination and resampling to obtain an optimized sample set;

[0074] In a specific implementation process, auxiliary indicators can be introduced to verify the reliability of the expansion rate distribution characteristics. Land use efficiency (such as the population carrying capacity per unit of construction land) and population growth elasticity coefficient (such as the ratio of population growth rate to expansion rate) are used for fitting analysis (such as calculating correlation coefficient or determining coefficient) with expansion rate distribution. If the fitting degree is lower than the preset fitting degree (for example, R²<0.7), it indicates that the sample may have outliers or noise, so outlier rejection (such as using box plot method or Z-score method) and resampling (such as random sampling or bootstrap) need to be performed to optimize the sample set and improve the accuracy of distribution characteristics.

[0075] d. The expansion rate distribution difference characteristics, time dimension adaptation parameter set and optimized sample set of each group are input into the pre-constructed adaptive threshold adjustment model for solving to obtain the optimal ratio index threshold interval of different urban-rural gradient zones and different time periods.

[0076] In a specific implementation process, an adaptive threshold adjustment model can be constructed. The model takes the expansion rate distribution characteristics (mean, standard deviation, skewness), time trend coefficient and auxiliary verification results as input, and performs multi-objective optimization through genetic algorithm, etc. to solve the optimal ratio index threshold interval (such as [0.9, 1.1] or dynamic interval). Genetic algorithm searches for the best threshold through selection, crossover and mutation operations, ensuring that the threshold can effectively distinguish the collaborative expansion mode in different gradient zones and time periods.

[0077] Specifically, the above scheme realizes the dynamic self-adaptive adjustment of the rate index threshold interval by systematically integrating the distribution characteristics, time evolution law and data quality optimization mechanism of the expansion rate of the urban-rural gradient zone. First, the expansion rate of each group is extracted to form a sample set according to the grouping of different urban-rural gradient zones. This step ensures that the sample set can accurately reflect the unique dynamic characteristics of each urban-rural gradient zone by grouping processing, avoiding the confusion of characteristics between urban-rural gradient zones caused by overall analysis. Second, the mean, standard deviation and skewness coefficient of each sample set are calculated to obtain the distribution difference characteristics of the expansion rate. These statistics together build a complete distribution image of the expansion rate of each urban-rural gradient zone, so that the threshold adjustment can be based on the actual distribution form rather than a fixed value, significantly improving the scientificity and pertinence of the threshold setting. Further, the time trend coefficient is obtained by fitting the change slope of the expansion rate in the continuous time period through a linear regression model, and the increasing, decreasing or stable trend is identified based on the coefficient to determine the time adaptation direction. This step uses historical data to capture the time dynamic evolution law, so that the threshold adjustment can actively respond to the long-term change trend of the expansion rate. In addition, the land use efficiency and population growth elasticity coefficient are used as auxiliary verification indicators, and when the fitting degree is lower than the preset threshold, the abnormal value is removed and resampled. This step verifies the rationality of the expansion rate distribution through external indicators, removes the noise-affected data through abnormal value removal, and ensures the representativeness of the sample through resampling, thereby enhancing the robustness of the threshold calculation. Finally, the distribution difference characteristics, time dimension parameter set and optimized sample set are input into the adaptive threshold adjustment model to solve the optimal threshold interval. This model optimizes the multi-dimensional dynamic information cooperatively, so that the threshold interval can accurately match the expansion state of each urban-rural gradient zone in a specific period, and finally realize the high-precision classification of the construction-traffic coordinated expansion mode, ensuring that the mode recognition result is highly consistent with the actual urban spatial evolution law.

[0078] In some embodiments, a certain region is selected as the experimental object, the research scope is divided with the municipal road network as the boundary, and the urban-rural gradient division method of the present application is used to establish the research unit. The construction land data is derived from the global land cover product GlobeLand30 (spatial resolution 30 m, providing multi-temporal data such as 2000, 2005, 2010, 2015, 2020), which can effectively reflect the spatial distribution characteristics of built-up areas at different periods. The road network data comes from the OpenStreetMap (OSM) platform, including road grade, node distribution, connectivity and road length attributes. Taking each urban-rural gradient zone as the basic analysis unit, the multi-temporal construction land area and road total length data are extracted, the construction land growth rate, road growth rate and construction-traffic ratio index are calculated, and the coordinated expansion mode of each urban-rural gradient zone is identified in combination with the two-dimensional discrimination framework. The multi-period urban-rural gradient division results are shown in Figure 4 .Figure 4 is a multi-period urban-rural gradient division result map, wherein each 5 years can be taken as a division period. The construction land expansion analysis result is as shown in Figure 5 Figure 5 is a construction land expansion analysis result map, the horizontal coordinate is a time period, and the vertical coordinate is a construction land area. The proportion of different expansion modes is as shown in Figure 6 Figure 6 is a result map of the proportion of different expansion modes. Through the method, the coordinated evolution characteristics of construction land and traffic network in different functional areas of a city can be intuitively displayed.

[0079] Based on the same overall inventive concept, the present application also protects a construction land and traffic network coordinated expansion mode measurement system. The construction land and traffic network coordinated expansion mode measurement system provided by the present application is described as follows, and the construction land and traffic network coordinated expansion mode measurement system described below can be correspondingly referred to the construction land and traffic network coordinated expansion mode measurement method described above.

[0080] Figure 7 is a structural schematic diagram of the construction land and traffic network coordinated expansion mode measurement system provided by the embodiment of the present application, as shown in Figure 7 The construction land and traffic network coordinated expansion mode measurement system of the embodiment includes an acquisition module 71, a first calculation module 72, a division module 73, a second calculation module 74, a third calculation module 75 and a generation module 76.

[0081] The acquisition module 71 is configured to acquire construction land data, traffic network data and population data with the same time period identifier in a research area, and perform spatial consistency processing to establish a spatial correlation relationship of the construction land data, the traffic network data and the population data in different spatial units, so as to obtain structured data consistent in time and space.

[0082] The first calculation module 72 is configured to calculate population density and construction land density of each spatial unit in the research area based on the structured data.

[0083] The division module 73 is configured to divide the research area into a plurality of urban-rural gradient zones using a clustering algorithm based on the population density and the construction land density, and assign an attribute label to each urban-rural gradient zone; the attribute label includes population density, construction land proportion, road density and road grade.

[0084] The second calculation module 74 is configured to calculate a construction land growth rate and a road growth rate of each urban-rural gradient zone in a plurality of time periods by taking the urban-rural gradient zone as a basic analysis unit.

[0085] ​​The third calculation module 75 is configured to calculate a ratio index of construction and traffic in each urban-rural gradient zone based on the construction land growth rate and the road growth rate.

[0086] The generation module 76 is configured to classify each urban-rural gradient zone into a coordinated expansion mode based on the ratio index and the road growth rate, and generate a mode distribution result.

[0087] Figure 8 Fig. 1 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. The electronic device can include a processor 810, a communications interface 820, a memory 830, and a communications bus 840. The processor 810, the communications interface 820, and the memory 830 can communicate with each other through the communications bus 840. The processor 810 can invoke a logical instruction in the memory 830 to execute a coordinated expansion mode measurement method of construction land and a traffic network.

[0088] In addition, the logical instruction in the memory 830 described above can be implemented in the form of a software function unit and sold or used as an independent product. When used, the logical instruction can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0089] It should be noted that the related information involved in the various embodiments of the present application is strictly in accordance with the requirements of laws and regulations, and follows the principles of legality, legitimacy, and necessity, and is based on the reasonable purposes of business scenarios, processes the information provided by the user in the process of using the product / service or generated due to the use of the product / service, and the information authorized by the user.

[0090] The related information processed by the present application will be different due to specific product / service scenarios, and the specific scenarios of the user using the product / service will be used as the standard. It may involve the user's account information, device information or other related information. The present application will treat the related information and its processing with a high degree of diligence and obligation.

[0091] The present application pays great attention to the security of relevant information, and has taken reasonable and feasible security protection measures in line with industry standards to protect the information of the user and prevent the relevant information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0092] The device embodiments described above are only illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0093] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0094] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for measuring the coordinated expansion pattern of construction land and transportation networks, characterized in that, include: Data on construction land, transportation network, and population with the same time period identifiers within the study area are acquired and spatially consistent. Spatial correlation relationships between construction land, transportation network, and population data in different spatial units are established to obtain spatiotemporally consistent structured data. Based on the structured data, the population density and construction land density of each spatial unit within the study area are calculated; Based on the population density and the construction land density, a clustering algorithm is used to divide the study area into multiple urban-rural gradient zones, and each urban-rural gradient zone is assigned an attribute label; the attribute label includes population density, construction land ratio, road density, and road grade; Using the urban-rural gradient zones as the basic analysis unit, the growth rate of construction land and the growth rate of roads in each urban-rural gradient zone are calculated over multiple time periods. Based on the aforementioned construction land growth rate and road growth rate, calculate the construction-to-transport ratio index in each urban-rural gradient zone; Based on the ratio index and road growth rate, the coordinated expansion pattern of each urban-rural gradient zone is classified, and the pattern distribution results are generated.

2. The method for measuring the coordinated expansion pattern of construction land and transportation networks according to claim 1, characterized in that, Spatial consistency processing is performed to establish spatial relationships between construction land data, transportation network data, and population data within different spatial units, resulting in spatiotemporally consistent structured data, including: Based on a unified spatial reference system and resolution, spatial matching and boundary clipping are performed on construction land data, transportation network data, and population data to ensure that construction land data, transportation network data, and population data are consistent in spatial scope and coordinate system. By using spatial overlay analysis, spatial relationships between construction land data, transportation network data, and population data are established, resulting in spatiotemporally consistent structured data.

3. The method for measuring the coordinated expansion pattern of construction land and transportation networks according to claim 1, characterized in that, Based on the population density and the construction land density, a clustering algorithm is used to divide the study area into multiple urban-rural gradient zones, including: Clustering algorithms are used to comprehensively classify population density and construction land density, forming a continuous distribution sequence from highly urbanized to non-urbanized areas; Based on the comprehensive classification results, the study area is divided into multiple urban-rural gradient zones, and spatial vector boundaries are generated to ensure seamless coverage and non-overlap between the urban-rural gradient zones. Among them, the urban-rural gradient zones include at least two of the following: urban center area, densely built area, semi-dense area, urban-rural transition area, rural settlement area, scattered rural area and uninhabited area.

4. The method for measuring the coordinated expansion pattern of construction land and transportation networks according to claim 1, characterized in that, Calculate the growth rate of construction land and road development for each urban-rural gradient zone over multiple time periods, including: Extract the area of ​​construction land and the total length of roads for each time period; Calculate the ratio of the construction land area in adjacent time periods to the construction land area in the initial time period, and use it as the construction land growth rate. The ratio of the total road length in adjacent time periods to the total road length in the initial time period is calculated as the road growth rate. The growth rates of construction land and roads in each urban-rural gradient zone are arranged in chronological order to form a time series matrix.

5. The method for measuring the coordinated expansion pattern of construction land and transportation networks according to claim 1, characterized in that, Based on the aforementioned construction land growth rate and road growth rate, calculate the construction-to-transport ratio index for each urban-rural gradient zone, including: The ratio of the growth rate of construction land to the growth rate of roads is used as the ratio index; Based on the aforementioned construction land growth rate and road growth rate, after calculating the construction-to-transport ratio index for each urban-rural gradient zone, the following is also included: The ratio index is correlated with the urban-rural gradient zone, and the ratio index of different urban-rural gradient zones at different time periods is statistically and visually analyzed. Output the ratio index of each urban-rural gradient zone and the time series variation characteristics of the ratio index to form a database of construction and transportation coordination index.

6. The method for measuring the coordinated expansion pattern of construction land and transportation networks according to claim 1, characterized in that, Based on the aforementioned ratio index and road growth rate, a collaborative expansion pattern classification is performed for each urban-rural gradient zone, generating pattern distribution results, including: The ratio index is used to characterize the relative rate of construction expansion with respect to traffic evolution, and the road growth rate is used to characterize the absolute direction of change in the traffic system. Set a threshold range for the ratio index, and dynamically adjust the threshold range for the ratio index based on the distribution characteristics of the expansion rate of construction land and transportation networks; Based on the threshold range of the ratio index and the absolute direction corresponding to the road growth rate, the collaborative expansion patterns are classified, and the pattern distribution results are generated.

7. The method for measuring the coordinated expansion pattern of construction land and transportation networks according to claim 6, characterized in that, Based on the ratio index threshold range and the absolute direction corresponding to the road growth rate, collaborative expansion patterns are classified, and pattern distribution results are generated, including: When the road growth rate is greater than zero and the ratio index is greater than the upper limit of the ratio index threshold range, it is classified as a construction-driven type. When the road growth rate is greater than zero and the ratio index is between the lower limit and the upper limit of the ratio index threshold range, it is classified as a coordinated expansion type. When the road growth rate is greater than zero and the ratio index is less than the lower limit of the ratio index threshold range, it is classified as traffic-dominated.

8. The method for measuring the coordinated expansion pattern of construction land and transportation networks according to claim 6, characterized in that, The threshold range of the ratio index is dynamically adjusted based on the distribution characteristics of the expansion rate of construction land and transportation networks, including: The urban and rural gradient zones are grouped by type, and the expansion rates of construction land and roads in all time periods within each group are extracted to form a sample set for each group. The mean, standard deviation and skewness coefficient of each sample set are calculated to obtain the distribution characteristics of expansion rates of different urban and rural gradient zones. By fitting the slope of the expansion rate change of each group of samples in a continuous period of time using a linear regression model, the temporal trend coefficient is obtained. Based on the temporal trend coefficient, the increasing, decreasing or stable trend of the expansion rate is identified, the time adaptation direction of the threshold adjustment is determined, and a time dimension adaptation parameter set is formed. Land use efficiency and population growth elasticity coefficient are used as auxiliary verification indicators. When the goodness of fit between the auxiliary indicators and the expansion rate distribution characteristics is lower than the preset goodness of fit, outlier removal and resampling are performed on the sample set to obtain an optimized sample set. The expansion rate distribution differences of each group, the time dimension adaptation parameter set, and the optimized sample set are input into a pre-built adaptive threshold adjustment model for solution, so as to obtain the optimal ratio index threshold range for different urban and rural gradient zones and different time periods.

9. The method for measuring the coordinated expansion pattern of construction land and transportation network according to any one of claims 1-8, characterized in that, Also includes: Statistical analysis was conducted on various coordinated expansion patterns within the same urban-rural gradient zone to calculate the spatial proportion and distribution characteristics of different coordinated expansion patterns. Generate spatial distribution maps of the coordinated expansion patterns in each urban-rural gradient zone, and use color and / or symbols to distinguish different coordinated expansion patterns.

10. A measurement system for the coordinated expansion pattern of construction land and transportation networks, characterized in that, include: The acquisition module is used to acquire construction land data, transportation network data and population data with the same time period identifier within the study area, and to perform spatial consistency processing to establish spatial correlation relationships between construction land data, transportation network data and population data in different spatial units, thereby obtaining spatiotemporally consistent structured data. The first calculation module is used to calculate the population density and construction land density of each spatial unit within the study area based on the structured data. The segmentation module is used to divide the study area into multiple urban-rural gradient zones based on the population density and the construction land density using a clustering algorithm, and to assign attribute labels to each urban-rural gradient zone; the attribute labels include population density, construction land ratio, road density, and road grade; The second calculation module is used to calculate the construction land growth rate and road growth rate of each urban-rural gradient zone in multiple time periods, using the urban-rural gradient zone as the basic analysis unit. The third calculation module is used to calculate the ratio index of construction to transportation in each urban-rural gradient zone based on the construction land growth rate and road growth rate. The generation module is used to classify the coordinated expansion patterns of each urban-rural gradient zone based on the ratio index and road growth rate, and generate pattern distribution results.

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