Multi-source data-based urban circle city development and scientific and technological innovation coordination coupling level evaluation method

Abstract

The invention belongs to the technical field of urban planning and construction, and particularly relates to an urban circle city development and scientific and technological innovation coordination coupling level evaluation method based on multi-source data. The method mainly comprises an evaluation index system construction step, a data acquisition and preprocessing step, a comprehensive score determination step, a system coupling coordination degree calculation step and a system coupling interaction relation evaluation step. Aiming at the problem that the existing urban circle development evaluation neglects the coupling effect of scientific and technological innovation and urban development, the method comprises the following steps of: constructing an urban development and scientific and technological innovation evaluation index system, collecting multi-source data such as land utilization data and statistical yearbook data and preprocessing; and evaluating the relative development degree in the city and the development difference degree between the cities by using an entropy method and a coupling coordination degree model. The method breaks through the limitation of traditional single-dimension and single-tool evaluation, can effectively reveal the interaction rule of an urban circle'city-department and creative 'composite system, and provides a practical basis for urban circle innovation resource allocation and urban development planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban planning and construction, and specifically relates to a method for evaluating the coordinated coupling level of urban development and technological innovation in a metropolitan area based on multi-source data. Background Art

[0002] As a crucial component of the spatial dynamics of regional high-quality development, metropolitan areas play a vital role in driving scientific and technological innovation breakthroughs, effectively expanding domestic demand, and connecting with the world to unlock the benefits of opening up. With the accelerating evolution of a new round of scientific and technological revolution and industrial transformation, how to effectively mobilize and integrate various innovative resources within metropolitan areas and achieve high-quality coordinated development between urban functions and scientific and technological innovation has become a key topic of discussion within the scientific community. Therefore, systematically evaluating the coordinated development of "city-scientific and technological innovation" within metropolitan areas not only helps to reveal the inherent laws of complex system interactions within and between cities within metropolitan areas, but also has far-reaching strategic significance for fully leveraging the regional economic radiation and driving role of metropolitan areas and achieving complementary advantages and coordinated development among cities.

[0003] With the accelerated pace of urbanization, metropolitan areas, as a crucial component of the spatial dynamics of regional high-quality development, play a vital role in leading scientific and technological innovation breakthroughs, effectively expanding domestic demand, and connecting with the world to unleash the benefits of opening up. Currently, research evaluating metropolitan area development primarily focuses on constructing evaluation indicators such as regional scale, economic development, innovation momentum, population vitality, and ecological environment. It also focuses on the spatial competition and cooperation within metropolitan areas in areas such as economy, transportation, market, and labor, while neglecting the coupling effect between regional scientific and technological innovation cooperation and the development levels of large, medium, and small cities as scientific and technological innovation entities accelerate their concentration in metropolitan areas. Furthermore, existing research often utilizes evaluation methods such as the analytic hierarchy process (AHP), fuzzy comprehensive evaluation, coefficient of variation, and factor analysis, while few studies have combined entropy methods and coupling coordination models to deeply examine the spatial imbalances of the "city-science and innovation" complex system within metropolitan areas. This makes it difficult to reveal the coupling effect between regional scientific and technological innovation cooperation and the development levels of large, medium, and small cities, and thus fails to effectively guide the allocation of innovation resources and urban development planning within metropolitan areas.

[0004] Currently, there are the following problems regarding urban development and technological innovation: 1. The lack of in-depth analysis of the coordinated coupling relationship between "urban development" and "scientific and technological innovation" in metropolitan areas makes it difficult to reveal the coupling effect between regional scientific and technological innovation cooperation and the development levels of large, medium and small cities, and thus cannot effectively guide the allocation of innovation resources and urban development planning and construction in metropolitan areas.

[0005] 2. Existing evaluation methods often use single tools such as the analytic hierarchy process, fuzzy comprehensive evaluation method, coefficient of variation method, and factor analysis. They lack the ability to combine entropy methods with coupling coordination models to deeply study the spatial imbalance of the "city-sci-tech innovation" complex system in metropolitan areas.

[0006] 3. Existing research mainly focuses on 4. The construction of single-dimensional evaluation indicators such as regional scale, economic development, innovation enthusiasm, population vitality, and ecological environment lacks the comprehensiveness of incorporating urban development level and scientific and technological innovation cooperation into a unified evaluation framework.

[0007] 4. Existing research focuses more on the spatial competition and cooperation of metropolitan areas in the fields of economy, transportation, market, and labor, but ignores the coupling effect between regional scientific and technological innovation cooperation and the development levels of large, medium and small cities as scientific and technological innovation entities accelerate their agglomeration towards metropolitan areas.

[0008] 5. Existing research lacks the ability to conduct a comprehensive evaluation of the "city-sci-tech innovation" complex system in metropolitan areas based on multi-source data, such as administrative boundary vector data, land change survey data, and statistical yearbook data. Summary of the Invention

[0009] In view of this, the present invention provides a method for evaluating the coordinated coupling level of urban development and scientific and technological innovation in metropolitan areas based on multi-source data, comprising: Steps for constructing the evaluation index system: Construct a metropolitan area urban development evaluation index system and a metropolitan area scientific and technological innovation evaluation index system. The urban development evaluation index system covers indicators related to spatial scale, economic development, population growth, and industrial structure, while the scientific and technological innovation evaluation index system covers indicators related to innovation facilities and innovation output; Data acquisition and preprocessing steps: Acquire multi-source data related to urban development and technological innovation in the metropolitan area, preprocess each type of data separately, and integrate them to build a metropolitan area urban spatial database; Steps for determining the comprehensive score: standardize the evaluation index data of the coordinated development of urban development and scientific and technological innovation, use the objective weighting method to determine the weight of each indicator, and then calculate the comprehensive score of urban development and scientific and technological innovation; System coupling and coordination degree calculation steps: Based on the above comprehensive scores, the coupling degree and comprehensive coordination index of urban development and scientific and technological innovation are calculated, and then the coupling coordination degree is obtained and graded; Evaluation steps for system coupling interaction: Based on the comprehensive score of urban development and scientific and technological innovation and the coupling coordination degree, the relative development level within the city and the degree of development differences between cities are evaluated.

[0010] Furthermore, in the construction of the urban development evaluation index system of the metropolitan area, the data on the construction land area, a secondary indicator of spatial scale, comes from the land change survey data published by the land survey results sharing application service platform. It is calculated as the sum of the areas of various land types such as urban land, town land, and village land.

[0011] Furthermore, in the urban agglomeration city development evaluation index system, in the economic development index, per capita GDP is calculated by dividing regional GDP by permanent population, and per-unit GDP is calculated by dividing regional GDP by administrative area. Per capita consumption expenditure of urban households is directly obtained from the statistical yearbooks of each city; in the population growth index, permanent population and urbanization rate of permanent population are directly obtained from the statistical yearbooks of each city; in the industrial structure index, the added value of the secondary industry is directly obtained from the statistical yearbooks of each city; In the metropolitan area science and technology innovation evaluation index system, the secondary indicators of innovation facilities, the number of colleges and universities, the number of key laboratories, and the number of scientific research and technical service institutions are directly obtained from the statistical yearbooks of each city, and the number of new R&D institutions and the number of high-tech enterprises are obtained from the statistical yearbooks and statistical bureaus of each city; in the innovation output indicators, the number of invention patent authorizations and the number of valid invention patents are directly obtained from the statistical yearbooks of each city.

[0012] Furthermore, in the data acquisition and preprocessing steps, urban development data includes administrative boundary vector data obtained from Tiandi Map, construction land area data obtained from the land survey results sharing application service platform, and multiple attribute data obtained from the statistical yearbooks of each city and the calculation of derived data; scientific and technological innovation data is obtained from the statistical yearbooks of each city; and ArcGIS software is used to perform spatial association to construct a database.

[0013] Furthermore, when obtaining administrative boundary vector data from Tiandi Map, Python is used in combination with the crawler library, reasonable request headers are set to simulate browser access, and the geographic information processing library is used to convert the format and check the data integrity.

[0014] Furthermore, in the step of determining the comprehensive score, in the SPSSAU software, the data exported from the ArcGIS software is first subjected to extreme value normalization processing, the data is compressed to the interval [0.01, 0.99], and then the entropy method is used to determine the indicator weights and calculate the comprehensive score.

[0015] Furthermore, when performing standardization processing in SPSSAU software, the forward tab of the Generate Variables dialog box is used, and the descriptive statistics function is used to view the data distribution after processing; when using the entropy method, the parameters can be adjusted according to the actual situation, and the weight coefficient can be analyzed after calculation to optimize the indicator system.

[0016] Furthermore, in the step of calculating the system coupling coordination degree, the coupling degree, the comprehensive coordination index and the coupling coordination degree are calculated in the SPSSAU software according to a specific formula, and the results are exported to the ArcGIS software for spatial visualization.

[0017] Furthermore, in the step of evaluating the system coupling interaction relationship, data is exported from ArcGIS software to SPSSAU software to calculate the relative development degree within the city and the degree of development difference between cities, and corresponding development strategies are formulated based on the calculation results.

[0018] The beneficial effects of the present invention are: Compared with the existing technology, the present invention provides a method for evaluating the coordinated coupling level between urban development and scientific and technological innovation in metropolitan areas based on multi-source data, which has the following beneficial effects: 1. This study breaks through the limitations of traditional single-dimensional evaluation frameworks by incorporating urban development levels and scientific and technological innovation cooperation into a unified evaluation framework. By analyzing the dynamic coupling mechanism between cities of different sizes and the flow of scientific and technological innovation resources, it reveals how "urban development" and "innovation upgrading" in metropolitan areas promote each other. This fills the gap in previous research on the coordinated coupling of scientific and technological innovation among large, medium and small cities in a region, and improves the reliability and comprehensiveness of the evaluation results. 2. This method breaks through the limitations of traditional single-tool evaluation models and innovatively adopts a combined approach of "entropy method + coupling coordination model" to combine multi-source data to quantify the spatial imbalance characteristics of the "city-sci-tech innovation" complex system. This effectively reveals the degree of matching between scientific and technological innovation and comprehensive development of cities within the metropolitan area, overcomes the difficulty of traditional evaluation models in reflecting the dynamic interactions of complex systems, and improves the objectivity and accuracy of the evaluation results. 3. Based on multi-source data such as administrative boundary vector data, land change survey data, and statistical yearbook data, a metropolitan area urban spatial database was established. This provided data support for evaluating the coordinated coupling level of the metropolitan area's "city-sci-tech innovation" complex system, improving the reliability of the evaluation results. 4. By evaluating the relative development levels within cities and the degree of development differences between cities, the paper reveals the imbalance in coordination between cities within the metropolitan area and within cities in terms of urban development and scientific and technological innovation, providing a practical basis for the subsequent allocation of innovation resources and urban development planning and construction in the metropolitan area. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the technical route of the method for evaluating the coordinated coupling level of urban development and technological innovation in metropolitan areas based on multi-source data of the present invention; Figure 2 This is a schematic diagram of the average GDP per district (city / county) in a certain metropolitan area in 2022 according to an embodiment of the present invention; Figure 3This is a schematic diagram of the permanent population distribution of each district (city / county) in a certain metropolitan area according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the added value of the secondary industry in a certain metropolitan area (city / county) according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the distribution of colleges and universities in various districts (cities / counties) in a certain metropolitan area according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the distribution of invention patent authorizations in various districts (cities / counties) of a certain metropolitan area according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the distribution of the coordinated coupling degree between urban development and scientific and technological innovation in various districts (cities / counties) of a certain metropolitan area in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To further illustrate the specific implementation process of the present invention, a detailed process description is now given as follows: Step 1: Data collection and preprocessing Step 1-1: Collect and preprocess urban development data.

[0021] Acquisition and processing of administrative boundary vector data: Using the Python programming language combined with relevant web crawler libraries (such as BeautifulSoup and requests), a crawler program was developed based on the Tiandi Map page structure. Appropriate request headers were set within the program to simulate browser access and prevent the website from identifying it as an illegal access. Following the list of cities in the metropolitan area, links to administrative boundary data were searched for one by one on Tiandi Map to obtain vector data. After acquisition, the data was converted to the .shp format, which can be directly read by ArcGIS software, using a geographic information processing library (such as GDAL). During the conversion process, the data integrity was carefully checked to ensure that the boundary data was complete and the topology structure was correct.

[0022] Land Change Survey Data Screening: Log in to the Land Survey Results Sharing Application Service Platform and accurately filter the corresponding land change survey data from the platform database based on the administrative region codes of each city in the metropolitan area. Use database query statements (such as SQL SELECT statements) to extract construction land area attribute data. To ensure data accuracy, perform preliminary logical checks on the extracted data, such as checking whether the area data is positive and whether there are any abnormally large or small values.

[0023] Extracting Statistical Yearbook Data and Calculating Derivative Indicators: Download the latest statistical yearbook PDF file from the official statistical bureau website of each city. Use OCR text recognition software (such as ABBYY FineReader) to convert the table data in the PDF into an editable Excel format. In Excel, use data validation to ensure that the extracted basic data, such as administrative area and permanent population, are correctly formatted and contain no duplicate values. Using the formulas (GDP per capita = GDP per region / permanent population, GDP per square meter = GDP per region / administrative area), use Excel's formula calculation function to accurately calculate derived indicators. After calculations are completed, recheck the results to prevent formula reference errors or data input errors.

[0024] Data spatial association: Open ArcGIS software and create a new geographic database. Import the processed administrative boundary vector data, construction land area data, and data extracted and calculated from the statistical yearbook into the corresponding feature classes and tables of the geographic database. Using ArcGIS's spatial connection tool, based on the administrative boundary vector data, other attribute data are associated with the corresponding spatial location according to unique identifiers such as city names or administrative area codes to build a metropolitan area city spatial database. After the association is completed, use ArcGIS's attribute table browsing function to check the integrity and accuracy of the associated data to ensure that the attribute data of each city is accurately associated with the corresponding spatial elements. Figure 2-5 The figure shows the visualization effect after the urban development data of a certain metropolitan area in the embodiment is imported into ArcGIS.

[0025] The evaluation index system for urban development in metropolitan areas is as follows:

[0026] Step 1-2: Collect and preprocess scientific and technological innovation data.

[0027] Data Collection: Similar to the processing of statistical yearbook data for urban development data, data related to scientific and technological innovation was collected from the websites or statistical yearbooks of each city's statistical bureau. This data included the number of institutions of higher learning, key laboratories, new R&D institutions, scientific research and technical service institutions, high-tech enterprises, invention patent authorizations, and valid invention patents. Regarding the number of new R&D institutions, in addition to the statistical yearbooks, supplementary data was collected through the official websites of each city's science and technology bureaus, government bulletin boards, and other channels to ensure comprehensive data collection.

[0028] Data spatial association: In ArcGIS software, spatially associate the organized science and technology innovation data with the established metropolitan area city spatial vector data. The operation process is similar to that of urban development data association. Through common identifiers (such as city names or codes), use spatial connection tools to associate science and technology innovation data with corresponding city spatial locations to improve the metropolitan area city spatial database. After association, the association results are visually checked, for example, by displaying the distribution of different science and technology innovation indicators on the map, it is intuitively judged whether the data association is correct. Figure 6 The figure shows the visualization effect of the number of invention patent authorizations in each district (city / county) of a certain metropolitan area in the embodiment after it is imported into ArcGIS.

[0029] The table of evaluation index system for scientific and technological innovation in metropolitan areas is as follows:

[0030] Step 2: Determine the comprehensive score of urban development (scientific and technological innovation).

[0031] Step 2-1: Perform data extreme value normalization.

[0032] Considering that the evaluation indicators are all positive, in order to make the results comparable, (Suppose there are n cities and m indicators, Dij is the j-th indicator value of city i, i=1,2,3,…,n; j=1,2,3,…,m) formula, using extreme value normalization to linearly and proportionally radiate the value of Dij to the interval (0,1).

[0033] Export the data table (xlsx worksheet) of the evaluation index of coordinated development of urban development and scientific and technological innovation in ArcGIS software, import it into SPSSAU software, open the Generate Variables dialog box, select the "Forward" tab, click OK to perform standardization, and then select "Data Intervalization" next to "Start Analysis" to compress the data between [0.01, 0.99] to obtain the evaluation index D'ij of coordinated development of urban development and scientific and technological innovation.

[0034] Step 2-2: Calculate the weight of the evaluation index.

[0035] Based on the D'ij value after standardization in the previous step, the indicator weight is determined by applying the entropy method in the objective weight method.

[0036] By formula Calculate the weight of the jth indicator of city i among all indicators ; By formula Calculate the information entropy value of the j-th indicator; By formula Calculate the information utility value of the j-th indicator; By formula Calculate the weight coefficient of the j-th indicator.

[0037] In SPSSAU software, open the comprehensive evaluation dialog box, select the "Entropy Method" tab, drag all the processed data into the analysis box, check "Non-negative translation", and calculate the information entropy value, information utility value and weight coefficient of each indicator.

[0038] Step 2-3: Calculate the comprehensive score of urban development (scientific and technological innovation).

[0039] By formula Obtain a comprehensive score for urban development (scientific and technological innovation).

[0040] In SPSSAU software, open the comprehensive evaluation dialog box, select the "Entropy Method" tab, check "Comprehensive Score" to get the final comprehensive score , The larger the value, the higher the level of urban development (scientific and technological innovation) of city i.

[0041] Step 3: Calculate the coupling coordination degree between urban development and scientific and technological innovation in the metropolitan area.

[0042] Step 3-1: Calculate the coupling degree.

[0043] Combined with the city comprehensive score obtained in the previous step, the formula Calculate the coupling degree between urban development and technological innovation, where 、 These are the comprehensive scores of urban development and scientific and technological innovation calculated based on the entropy method.

[0044] Step 3-2: Calculate the comprehensive coordination index.

[0045] Based on the comprehensive score of urban development and scientific and technological innovation 、 , through the formula Calculate the comprehensive coordination index of urban development and scientific and technological innovation in the metropolitan area, 、 are the contribution rates of the urban development system and the scientific and technological innovation system to the synergy between the two. The sum of the two is 1, which can be regarded as equally important. Therefore, = =0.5.

[0046] Step 3-3: Calculate the coupling coordination degree.

[0047] Combining the coupling degree and comprehensive coordination index, the formula Calculate the coupling coordination degree between urban development and scientific and technological innovation in the metropolitan area, and assign graded values to it.

[0048] Coupling coordination level definition

[0049] In SPSSAU software, open the comprehensive evaluation dialog box, select the "Coupling Coordination Degree" tab, put the comprehensive scores of urban development and scientific and technological innovation into the "Analysis Item" box, put the city into the "Label" box, check "Start Analysis" and finally get the coupling degree. , comprehensive coordination index , coupling coordination The data obtained previously were exported from SPSSAU software in the form of an xls worksheet, and connected to the metropolitan area city spatial database of ArcGIS software based on the "city" field to perform spatial visualization of the coordination level. Figure 7 The figure shows a schematic diagram of the distribution of the coordinated coupling degree between urban development and scientific and technological innovation in each district (city / county) of a certain metropolitan area in an embodiment.

[0050] Step 4: Evaluate the coupled interactive relationship between urban development and scientific and technological innovation in the metropolitan area.

[0051] Step 4-1: Evaluate the relative development level within the city.

[0052] Combined score of urban development and technological innovation 、 , through the formula Calculate the relative development level of urban development and scientific and technological innovation. When X>1, it indicates that urban development is ahead of scientific and technological innovation. At this time, the key to optimizing the coupling coordination model lies in increasing investment in scientific and technological innovation; when X<1, the effect is the opposite.

[0053] Create a new field "Relative Development Degree" in the metropolitan area city spatial database and use the field calculator to obtain the relative development degree value.

[0054] Definition of relative development level

[0055] Step 4-2: Evaluation of the degree of development differences between cities Combined score of urban development and technological innovation 、 Coordination degree with urban development and technological innovation , respectively, through the formula Calculate the regional difference in urban development between city i and city j in the metropolitan area; Calculate the regional difference in technological innovation between city i and city j within the metropolitan area. 、 The further away from 1, the more obvious the regional differences.

[0056] The data table of evaluation indicators for the coordinated development of urban development and scientific and technological innovation (xlsx worksheet) was exported from ArcGIS software and imported into SPSSAU software. The formula was used to calculate the regional differences in urban development, the regional differences in scientific and technological innovation, and the regional differences in the degree of coupling coordination between urban development and scientific and technological innovation.

Claims

1. A method for evaluating the coordinated coupling level between urban development and scientific and technological innovation in metropolitan areas based on multi-source data, characterized by: include: Steps for constructing the evaluation index system: Construct a metropolitan area urban development evaluation index system and a metropolitan area scientific and technological innovation evaluation index system. The urban development evaluation index system covers indicators related to spatial scale, economic development, population growth, and industrial structure, while the scientific and technological innovation evaluation index system covers indicators related to innovation facilities and innovation output; Data acquisition and preprocessing steps: Acquire multi-source data related to urban development and technological innovation in the metropolitan area, preprocess each type of data separately, and integrate them to build a metropolitan area urban spatial database; Steps for determining the comprehensive score: standardize the evaluation index data of the coordinated development of urban development and scientific and technological innovation, use the objective weighting method to determine the weight of each indicator, and then calculate the comprehensive score of urban development and scientific and technological innovation; System coupling and coordination degree calculation steps: Based on the above comprehensive scores, the coupling degree and comprehensive coordination index of urban development and scientific and technological innovation are calculated, and then the coupling coordination degree is obtained and graded; Evaluation steps for system coupling interaction: Based on the comprehensive score of urban development and scientific and technological innovation and the coupling coordination degree, the relative development level within the city and the degree of development differences between cities are evaluated.

2. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 1 is characterized in that: In the construction of the urban development evaluation index system of metropolitan areas, the data on construction land area, a secondary indicator of spatial scale, comes from the land change survey data published by the land survey results sharing application service platform. It is calculated as the sum of the areas of various land types, including urban land, town land, and village land.

3. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 1 is characterized by: In the urban agglomeration city development evaluation index system, among the economic development indicators, per capita GDP is calculated by dividing regional GDP by permanent population, and per-unit GDP is calculated by dividing regional GDP by administrative area. Per capita consumption expenditure of urban households is directly obtained from the statistical yearbooks of each city. Among the population growth indicators, permanent population and urbanization rate of permanent population are directly obtained from the statistical yearbooks of each city. Among the industrial structure indicators, the added value of the secondary industry is directly obtained from the statistical yearbooks of each city. In the metropolitan area science and technology innovation evaluation index system, the secondary indicators of innovation facilities, the number of colleges and universities, the number of key laboratories, and the number of scientific research and technical service institutions are directly obtained from the statistical yearbooks of each city, and the number of new R&D institutions and the number of high-tech enterprises are obtained from the statistical yearbooks and statistical bureaus of each city; in the innovation output indicators, the number of invention patent authorizations and the number of valid invention patents are directly obtained from the statistical yearbooks of each city.

4. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 1 is characterized in that: In the data acquisition and preprocessing steps, urban development data includes administrative boundary vector data obtained from Tiandi Map, construction land area data obtained from the land survey results sharing application service platform, and multiple attribute data obtained from the statistical yearbooks of various cities and the calculation of derived data; scientific and technological innovation data is obtained from the statistical yearbooks of various cities; and ArcGIS software is used to perform spatial association to construct a database.

5. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 4 is characterized in that: When obtaining administrative boundary vector data from Tiandi Map, Python is used in combination with the crawler library, reasonable request headers are set to simulate browser access, and the geographic information processing library is used to convert the format and check the data integrity.

6. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 1 is characterized in that: In the step of determining the comprehensive score, in SPSSAU software, the data exported from ArcGIS software is first subjected to extreme value normalization processing, the data is compressed to the interval [0.01, 0.99], and then the entropy method is used to determine the indicator weights and calculate the comprehensive score.

7. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 6 is characterized in that: When performing standardization processing in SPSSAU software, use the forward tab of the Generate Variables dialog box and use the descriptive statistics function to view the data distribution after processing; when using the entropy method, adjust the parameters according to the actual situation and analyze the weight coefficient after calculation to optimize the indicator system.

8. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 1 is characterized in that: In the system coupling coordination degree calculation step, the coupling degree, comprehensive coordination index and coupling coordination degree are calculated according to a specific formula in the SPSSAU software, and the results are exported to the ArcGIS software for spatial visualization.

9. The method for evaluating the level of coordinated coupling between urban development and technological innovation in metropolitan areas based on multi-source data according to claim 1 is characterized in that: In the step of evaluating the system coupling interaction relationship, data is exported from ArcGIS software to SPSSAU software to calculate the relative development degree within the city and the degree of development difference between cities, and corresponding development strategies are formulated based on the calculation results.

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