Sensitive ecosystem service flow analysis system and method for multi-source spatio-temporal big data

Abstract

The present application relates to a sensitive ecosystem service flow analysis system and method for multi-source spatio-temporal big data, which comprises a geographic information module, a data processing module, a model construction module and a service flow analysis module; the geographic information module is used to construct an evaluation unit; the data processing module is used to analyze and process the multi-source spatio-temporal big data of the geographic block to obtain target data; the model construction module fits the data to generate a decay analysis model; the service flow analysis module is used to call the decay analysis model, conduct spatial diffusion analysis according to the potential supply capacity of the service supply unit to determine the potential supply amount of the evaluation unit, and map the results to the geographic block. The present application can fully embody the three elements of supply side, demand side and ecological service flow dynamics, effectively avoid sample bias, and improve the accuracy and robustness of public space dynamic analysis.

Description

Technical Field

[0001] This invention relates to the field of intelligent measurement technology of urban public space ecosystems, and in particular to a sensitive ecosystem service flow analysis system and method based on multi-source spatiotemporal big data. Background Technology

[0002] Public space refers to urban spaces used by the public, such as streets, squares, and parks. As a core element of urban spatial organization, urban public space is an important spatial carrier for people's lives and plays a vital role in enhancing urban vitality and optimizing urban functions and quality.

[0003] Ecosystem services refer to all direct and indirect benefits that humans derive from ecosystems, including provisioning services, regulating services, cultural services, and support services. They are fundamental to maintaining regional ecological security and sustainable development. The realization of ecosystem services depends on the spatial distribution characteristics of natural elements and the dynamic effects of ecological processes. Ecosystems in different regions exhibit significant differences in their service provision capacity, manifesting as spatial imbalances. This disparity makes it difficult to directly translate the potential supply of ecosystem services into usable value, easily leading to spatial mismatches between supply and demand, thus affecting the efficient utilization of ecological resources. Sensitive ecosystems are those whose supply, flow, or value is highly dependent on specific geographical spatial patterns, locational relationships, and spatial scales.

[0004] To achieve spatial matching of ecosystem service supply and demand, existing technologies typically employ ecosystem service flow models to describe the transfer process of ecosystem services from the supply side to the demand side. This model, as a crucial technical means connecting supply and demand, requires spatial pathways to realize the transfer of service functions and value transformation. The realization of ecosystem service value is jointly influenced by supply-side characteristics, demand-side characteristics, and service flow characteristics. However, a unified and operational analytical framework has not yet been established, making it difficult to systematically characterize the dynamic relationship among supply, demand, and service flow. Existing technologies largely focus on supply-side ecological function assessment, with insufficient research on the spatial transfer characteristics of ecosystem service flows and the generation mechanism of demand, making it difficult to support refined assessment and analysis.

[0005] In ecosystem service flow modeling, existing technologies have not yet established standardized distance attenuation quantification models, making it difficult to accurately characterize the transmission patterns of ecosystem services as spatial distance changes. Existing methods cannot effectively identify the influencing factors of service flow attenuation, leading to significant uncertainty in the calculation results of transmission efficiency and affecting the accuracy of quantifying ecosystem service value. Furthermore, for spatially sensitive ecosystem services, existing technologies lack comprehensive demand assessment methods that combine population characteristics, socioeconomic conditions, and urban functional elements. They cannot transform the service needs of different groups into calculable parameter indicators, nor can they clearly define the contribution weight of each factor to service value, thus limiting the accuracy of supply-demand matching.

[0006] In terms of data acquisition and processing, existing technologies mainly rely on manual surveys and sampling statistics, which suffer from problems such as large workload, strong subjectivity, and insufficient spatial coverage. Although participatory crowdsourcing methods can expand sample sources, the sample distribution is concentrated and lacks representativeness, making it difficult to support large-scale engineering applications. While passively sensed data (such as mobile phone signaling, traffic card swipes, and social media location data) has high timeliness and wide coverage, current technologies cannot effectively integrate it with ecological supply and demand data, lacking a systematic "supply-service flow-demand" linkage model, which makes it difficult to fully realize the value of multi-source data.

[0007] Furthermore, existing technologies lack a comprehensive analytical framework that can be implemented, making it difficult to accurately identify spatial areas where the supply and demand of ecosystem services are mismatched. It is also impossible to determine whether the mismatch is caused by insufficient supply, obstructed service flow, or a lack of effective demand. As a result, subsequent optimization strategies lack specificity and restrict the realization of the potential value of ecosystem services.

[0008] In summary, existing technologies for realizing the value of ecosystem services suffer from several drawbacks, including inconsistent analytical frameworks, unclear service flow decay patterns, insufficient demand-side quantification, and limited data fusion capabilities. These issues lead to low accuracy in matching ecosystem service supply and demand, and unreliable assessment results, making it difficult to meet the demands for precise and scientific decision support in urban planning and ecological management. Therefore, it is necessary to propose a technical solution that enables accurate identification and matching of ecosystem service supply and demand to address these technical problems. Summary of the Invention

[0009] The technical problem to be solved by this invention is: in view of the technical problems existing in the prior art, this invention provides a sensitive ecosystem service flow analysis system and method based on multi-source spatiotemporal big data that can fully reflect the three elements of supply side, demand side and ecosystem service flow dynamics, effectively avoid sample bias, and improve the accuracy and robustness of dynamic analysis of public space.

[0010] To solve the above-mentioned technical problems, the technical solution proposed by this invention is: a sensitive ecosystem service flow analysis system for multi-source spatiotemporal big data, including a geographic information module, a data processing module, a model building module and a service flow analysis module; The geographic information module is used to delineate geographic blocks and display geographic region attributes. The geographic block includes service provisioning units and grid units, and an evaluation unit is constructed based on the grid units. The data processing module is used to analyze and process the multi-source spatiotemporal big data of the geographic block to obtain target data; the target data includes population data and access volume. The model building module includes a distance decay module, which contains one or more preset distance decay functions. The distance decay function fits the access volume and distance based on the supply volume of the service supply unit, and selects the distance decay function with the best fit as the decay analysis model of the distance decay module. The service flow analysis module is used to call the attenuation analysis model, perform spatial diffusion analysis based on the potential supply capacity of the service supply unit to determine the potential supply of the evaluation unit, and map the results to the geographic block.

[0011] Furthermore, the distance decay function of the model building module includes: (1) (2) (3) (4) In equations (1) to (4), The number of visits from the evaluated unit to the service provider unit. , , , , , These are the fitting parameters for the distance decay function. The distance between the evaluated unit and the service provision unit. For random variables, Indicates a normal distribution. The mean of the distances. denoted as the standard deviation of the distance.

[0012] Furthermore, the target data also includes economic data and geographical data; The model building module also includes a regression module, which contains one or more independent regression functions. The regression functions are used to fit the population data, economic data, geographical data, potential supply and access volume of the evaluated unit, and select the optimal regression function as the regression analysis model of the regression module. The service flow analysis module is also used to call the regression analysis model to analyze the access demand of the evaluation unit to the service supply unit under different potential supply, population, economic and geographical conditions.

[0013] Furthermore, the regression function includes the benchmark regression function: (5) Non-benchmark regression function: (6) (7) (8) In equations (5) to (8), The number of visits from the evaluated unit to the service provider unit. , These are the linear regression coefficients. As the dependent variable, Let be the ordinal number of the dependent variable. For regression coefficients, For linear regression error, , , , For the preset geographical weight matrix, For errors that do not have spatial autocorrelation, The regression coefficients are used; the dependent variables include population data, economic data, geographical data, and potential supply of the unit being evaluated.

[0014] Furthermore, the resolution of the grid cell is less than a preset resolution threshold; the geographic information module is also used to combine the grid cells into evaluation units based on geographic boundaries and / or distance.

[0015] A sensitive ecosystem service flow analysis method based on multi-source spatiotemporal big data includes the following steps: S1. Geographic blocks are delineated based on the geographic information system, service supply units and grid units are identified, and evaluation units are constructed based on the grid units; S2. Analyze and process the multi-source spatiotemporal big data of the geographic block to obtain target data; the target data includes population data and access volume; S3. Construct one or more preset distance decay functions, and fit the access volume and distance to the distance decay function based on the supply volume of the service supply unit, and use the distance decay function with the best fit as the decay analysis model. S4. Invoke the attenuation analysis model, perform spatial diffusion analysis based on the potential supply capacity of the service supply unit to determine the potential supply of the evaluation unit, and map the results to the geographic block.

[0016] Furthermore, the distance decay function includes: (1) (2) (3) (4) In equations (1) to (4), The number of visits from the evaluated unit to the service provider unit. , , , , , These are the fitting parameters for the distance decay function. The distance between the evaluated unit and the service provision unit. For random variables, Indicates a normal distribution. The mean of the distances. denoted as the standard deviation of the distance.

[0017] Furthermore, the target data also includes economic data and geographical data; Step S3 also includes constructing one or more regression functions, fitting the regression functions based on the population data, economic data, geographical data, potential supply and access volume of the evaluation unit, and selecting the optimal regression function as the regression analysis model. Step S4 also includes calling the regression analysis model to analyze the access demand of the assessment unit to the service supply unit under different potential supply, population, economic and geographical conditions.

[0018] Furthermore, the regression function includes the benchmark regression function: (5) Non-benchmark regression function: (6) (7) (8) In equations (5) to (8), The number of visits from the evaluated unit to the service provider unit. , These are the linear regression coefficients. As the dependent variable, Let be the ordinal number of the dependent variable. For regression coefficients, For linear regression error, , , , For the preset geographical weight matrix, For errors that do not have spatial autocorrelation, The regression coefficients are used; the dependent variables include population data, economic data, geographical data, and potential supply of the unit being evaluated.

[0019] Furthermore, the resolution of the grid cells is less than a preset resolution threshold; the grid cells are combined into evaluation cells based on geographical boundaries and / or distance.

[0020] Compared with the prior art, the advantages of the present invention are as follows: 1. This invention constructs attenuation analysis and regression models by fitting multi-source spatiotemporal big data, accurately depicting the relationship between the three elements of supply side, demand side and dynamic ecosystem service flow in sensitive ecosystems. It realizes the decomposition and coupling analysis of the entire process of value realization of service supply units, and can comprehensively, completely and accurately reflect the service flow logical relationship between service supply units and evaluation units, thereby more accurately assessing sensitive ecosystems.

[0021] 2. Based on multi-source spatiotemporal big data technology, this invention integrates multi-source social perception data with geospatial data, analyzes and processes large-scale human activity trajectories and spatial feature data according to standardized grid units, significantly improves the efficiency and accuracy of data acquisition, avoids sample selection bias caused by active collection, and further improves the accuracy of ecosystem service flow analysis.

[0022] 3. This invention fits data by constructing various distance decay functions, which can accurately identify the form of the decay function and complete parameter calibration according to different types of service supply units and urban public space characteristics; it handles spatial autocorrelation problems through regression functions and accurately quantifies demand-side influencing factors such as population and socio-economic status through multiple indicators, thereby improving the robustness of parameter estimation, thus more accurately reflecting the spatial propagation law of ecological service flow and improving the accuracy of demand analysis. Attached Figure Description

[0023] Figure 1This is a detailed flowchart illustrating the process of sensitive ecosystem service flow analysis based on multi-source spatiotemporal big data in a specific embodiment of the present invention.

[0024] Figure 2 This is a potential supply analysis diagram of parks in the area being analyzed, as described in a specific embodiment of the present invention.

[0025] Figure 3 This is a diagram illustrating the potential demand for the park from various groups within the analysis area, as described in a specific embodiment of the present invention.

[0026] Figure 4 This is a diagram illustrating the potential demand for parks from the residential population in the area being analyzed, as described in a specific embodiment of the present invention.

[0027] Figure 5 This is a diagram illustrating the potential demand for parks from working groups in the area being analyzed, as described in a specific embodiment of the present invention.

[0028] Figure 6 This is a diagram illustrating the potential demand for parks from different visitor groups in a specific embodiment of the present invention.

[0029] Figure 7 This is a coupled analysis diagram of the potential supply and access volume of the region being analyzed in a specific embodiment of the present invention.

[0030] Figure 8 This is a coupled analysis diagram of potential demand and access volume in a specific embodiment of the present invention, representing the area to be analyzed.

[0031] Figure 9 This is a coupled analysis diagram of potential supply and potential demand in the region being analyzed, as described in a specific embodiment of the present invention. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0033] In a specific embodiment, a park is taken as an example as a service supply unit of a sensitive ecosystem, and a certain city is taken as the analysis object to illustrate the technical solution of the present invention.

[0034] This embodiment of the multi-source spatiotemporal big data sensitive ecosystem service flow analysis system includes a geographic information module, a data processing module, a model building module, and a service flow analysis module. The geographic information module is used to delineate geographic blocks and display geographic region attributes. Geographic blocks include service supply units and grid units, and evaluation units are constructed based on the grid units. The data processing module is used to analyze and process the multi-source spatiotemporal big data of the geographic blocks to obtain target data. The target data includes population data and access volume. The model building module includes a distance decay module, which contains one or more preset distance decay functions. The distance decay function fits the access volume and distance based on the supply volume of the service supply unit, and selects the distance decay function with the best fit as the decay analysis model of the distance decay module. The service flow analysis module is used to call the decay analysis model, perform spatial diffusion analysis based on the potential supply capacity of the service supply unit to determine the potential supply volume of the evaluation unit, and map the results to the geographic blocks.

[0035] In this embodiment, a Geographic Information System (GIS) is selected to identify service provision units, divide grid units, and combine grid units to form evaluation units. Grids are divided in the GIS according to a preset resolution, and the park area is identified as a service provision unit based on the park's geographical boundaries. In this embodiment, service provision units and grid units do not overlap. This can be achieved through several methods: first, all grid unit areas overlapping with service provision units are considered service provision units; second, only all grid unit areas overlapping with service provision units are considered service provision units; third, based on the geographical boundaries of service provision units, overlapping grid units are further subdivided to determine service provision units and grid units. The preferred resolution threshold is greater than 100 meters and less than 500 meters; more preferably, it is greater than 200 meters and less than 300 meters. In this embodiment, grid units are divided using a rectangular method, and the resolution threshold is set to 250 meters, meaning each grid size is less than 250 meters * 250 meters. Furthermore, it is preferable to divide the grid units according to geographical boundaries, which include natural or artificially constructed boundaries such as rivers, roads, walls, and building boundaries. In this embodiment, the ArcGIS system is specifically used for basic geographic information processing. A total of 196 parks, including comprehensive parks, theme parks, community parks, and suburban parks, were identified through calibration. At a resolution of 250 meters × 250 meters, 333,720 grid units were determined. The grid units and service supply units uniformly adopt the WGS-84 coordinate system and are uniformly numbered, forming a data table containing fields such as unified number, unit vector data, grid boundary, and area to facilitate subsequent processing. In this embodiment, multi-source spatiotemporal big data includes spatiotemporal trajectory data, geographic data, economic data, and population data, which are acquired and used after legal authorization and anonymization. The sources, acquisition methods, and data types are diverse. For example, spatiotemporal trajectory data can be acquired through passive sensing methods such as mobile phone signaling, transportation card swiping, and social media positioning. When acquiring time trajectory data via mobile phone signaling, information such as device identifier, location, and duration of stay can be read. By determining whether the device's location is within a service supply unit and whether the stay duration exceeds a preset stay threshold (e.g., a stay threshold of 30 minutes means visits less than 30 minutes will not be considered valid), the number of visitors to the service supply unit can be effectively analyzed. The total number of visitors to the service supply unit can then be used to determine its supply volume. Population data can be acquired via mobile phone signaling or integrated with census data. In this embodiment, population data for grid units is acquired via mobile phone signaling, and the dwell status and park visits of the population in each grid unit are determined based on the mobile phone signaling.Specifically, by analyzing factors such as location, duration, and time spent in a place of residence through mobile phone signaling, the residence status of the population in each grid unit can be divided into three categories: residing, working, and visiting. Then, based on the category and total number of residence statuses, the population size and visitation status can be statistically analyzed. Geographic data can be obtained through Geographic Information Systems (GIS). For example, using ArcGIS's area tabulation tool, charts containing the area of ​​different land use types within each grid unit can be generated. Economic data such as housing prices, land prices, and production value can be obtained through economic censuses, real estate registration, and other relevant data.

[0036] In this embodiment, the raw data of multi-source spatiotemporal big data needs to be screened and cleaned, standardized, and invalid data is removed to ensure data accuracy. Data standardization includes, for example, projecting geographic data to a coordinate system such as WGS-84, using a geographic information system to eliminate spatial reference biases from different data sources. Economic data is standardized in units; for example, housing prices are standardized to the average housing price per square meter of the corresponding grid unit or evaluation unit; production value is standardized to the average production value per unit area of ​​the corresponding grid unit or evaluation unit; population data is standardized to the number of people per unit area; and the number of visits from grid units to service supply units is standardized to the average number of visitors per unit area per day. Invalid data, such as extreme values ​​exceeding a reasonable range, can be removed using the Z-score standard scoring method, with a threshold preferably set within ±3 to avoid outliers affecting the analysis results. After data preprocessing, the data is mapped to grid units and stored in the corresponding data items of the grid units in the data table.

[0037] In this embodiment, by acquiring, analyzing, and processing multi-source spatiotemporal big data, the average daily number of visitors to the service supply unit from each grid unit / evaluation unit can be obtained, i.e., the access volume. Then, by mapping the access volume to the grid units through the GIS system, the actual service flow of the service supply unit can be depicted. Based on the data distribution characteristics, the access volume is classified using the "natural discontinuity classification method" or the "quantile method," and different visual color schemes are assigned to different levels through a classification color scheme. This generates a spatial distribution map of the actual service flow, which is then displayed on the GIS to intuitively present the spatial distribution pattern of the actual service flow.

[0038] In this embodiment, the geographic information module is also used to combine grid cells into evaluation units based on geographic boundaries and / or distance. It should be noted that the combination method of evaluation units can be determined according to actual needs. The finer the evaluation unit division, the greater the storage and computational load required in the analysis, but the finer the analysis granularity and the more accurate the results. In this embodiment, it is preferable to combine grid cells into evaluation units based on geographic boundaries. Combining evaluation units based on geographic boundaries can realistically reflect the impact of road traffic conditions, rivers, walls, and other obstacles on access volume, making the evaluation results more accurate. In this embodiment, to reduce computational load, grid cells are combined into evaluation units according to a preset distance interval value, based on the distance between the grid cell and the service supply unit. The resulting evaluation unit is a distance concentric circle surrounding the service supply unit. The distance interval value is preferably consistent with the resolution threshold or an integer multiple of the resolution threshold. In this embodiment, the distance interval value is set to 250 meters.

[0039] In this embodiment, the model building module constructs four distance decay functions based on the number of visits and the distance. The distance decay functions include: (1) (2) (3) (4) In equations (1) to (4), The number of visits from the evaluated unit to the service provider unit. , , , , , These are the fitting parameters for the distance decay function. The distance between the assessed unit and the service provision unit. For random variables, Indicates a normal distribution. The mean of the distances. The standard deviation of the distance is denoted as . The sum of accesses to the service supply unit from all evaluation units represents the actual supply of that service supply unit. These four distance decay functions are independent of each other, each fitted based on access volume and distance. The best fit of each function is then selected as the decay analysis model for subsequent spatial diffusion analysis of the grid cells by the service supply units. It should be noted that the distance decay function is not limited to the four types listed above: exponential, power, log-normal, and Gaussian. Other distance decay functions can be constructed according to actual needs. In this embodiment, the best fit for a comprehensive park is the log-normal function, and the distance decay obtained after fitting the parameters is: goodness of fit For theme parks, the best fit is the log-normal function. The distance decay obtained after fitting the parameters is as follows: goodness of fit For community parks, the log-normal function provides the best fit. The distance decay obtained after determining the parameters through fitting is: goodness of fit For suburban parks, the power function is the best fit, and the distance decay obtained after determining the parameters through fitting is: goodness of fit The distance decay function with the best fit obtained is the decay analysis model determined by the distance decay module. Analysis of the curve fitting morphology shows that the distance decay function curves for comprehensive parks, theme parks, and community parks are non-monotonic, exhibiting a hump. This means that when closer to the park, the number of visitors per unit area increases with distance, but after this hump, it decreases with further distance. The hump for comprehensive and theme parks appears approximately 1 km from the park, while for community parks it appears approximately 1.5 km away. The distance decay pattern for suburban parks is monotonic, with the number of visitors per unit area decreasing with increasing distance. Furthermore, the distance decay pattern for some parks is non-monotonic, with the curve exhibiting a log-normal distribution. Furthermore, the data also shows that the long-tail effects of the four types of parks are inconsistent. By setting the radiation range to a distance of less than 1 visitor per square kilometer, comprehensive parks have a maximum radiation range of 20 kilometers, theme parks 15 kilometers, community parks 10 kilometers, and suburban parks 30 kilometers. Comprehensive parks, as the highest-level parks in urban areas, are attractive to people in areas slightly further from the city center. Theme parks mostly serve people within their own administrative region, community parks attract more residents from surrounding areas, and with the rise of outings, suburban parks, as popular weekend getaways for urban residents, have a radiation range farther than other types of parks. In this embodiment, the attenuation analysis model determines the relationship between the number of park visits by the evaluation unit / grid unit and distance. The sum of the number of park visits by all evaluation units / grid units represents the actual supply of the service supply unit. When factors such as the opening hours of a service supply unit change, causing a change in the total supply that the service supply unit can provide, i.e., its potential supply capacity, the potential supply of the service supply unit to each assessment unit / grid unit will also change linearly. Based on this linear relationship, the impact of changes in the potential supply capacity of the service supply unit on the potential supply of the assessment unit / grid unit can be analyzed. In this embodiment, the potential supply capacity of a service supply unit is determined based on factors such as its usable area, the number of users that can be accommodated per unit area, the daily effective opening hours, and the average duration of a single use. The potential supply capacity of a service supply unit = (usable area / number of users that can be accommodated per unit area) × (daily effective opening hours / average duration of a single use).Based on the actual conditions of the parks, the unit area capacity of suburban parks is 200 square meters per person, with an effective daily opening time of 8-10 hours and an average single usage time of 3-5 hours; comprehensive parks have 60 square meters per person, with an effective daily opening time of 8-10 hours and an average single usage time of 1-3 hours; theme parks have 30 square meters per person, with an effective daily opening time of 8-10 hours and an average single usage time of 1-2 hours; and community parks have 20 square meters per person, with an effective daily opening time of 24 hours and an average single usage time of 0.5-2 hours. The turnover rate (effective daily opening time / average single usage time) can be calculated based on the averages: suburban parks = 2, comprehensive parks = 4, theme parks = 6, and community parks = 24.

[0040] In this embodiment, based on the linear relationship between the potential supply capacity of service supply units and the potential supply of each evaluation unit / grid unit, spatial diffusion is performed according to the attenuation analysis model (the distance attenuation function with the best fit) of each park (service supply unit) and the potential supply capacity of the park to determine the potential supply of the park to the grid unit. Specifically, in this embodiment, multiple ring buffers are generated for each park at 250-meter intervals. Using ArcGIS's multi-ring buffer tool, the potential supply capacity of the park is set for spatial diffusion to obtain the potential supply of the park to the evaluation unit / grid unit under the set potential supply capacity.

[0041] In this embodiment, as described above, the population size at each dwelling state in the assessment unit / grid unit is determined through mobile phone signaling statistics, and this population size is used as the potential demand of this type of population for the service supply unit in the assessment unit / grid unit. Figure 4 , Figure 5 and Figure 6 The data in each assessment unit represents the potential demand (i.e., demand endowment) for the park from three groups: residents, workers, and visitors. Similarly, the total population represents the potential demand for the assessment unit / grid unit. Figure 3 As shown in the figure. By further classifying the potential demand of the evaluation unit / grid unit using the natural discontinuity classification method, a spatial distribution map of potential demand intensity can be generated. This map visually presents the spatial distribution pattern and gradient difference characteristics of the potential demand for service supply units within the region, providing visual support for supply and demand matching analysis.

[0042] In this embodiment, because the service flow of the population of the assessment unit / grid unit to park access is affected by both potential supply and demand, the service flow must not exceed the potential supply or the demand. As mentioned earlier, in this embodiment, after determining the potential supply based on the decay model, it is also necessary to couple the supply and demand sides. The coupling method is: potential service flow = MIN(potential supply, potential demand).

[0043] In this embodiment, coupling can determine three supply-demand matching states for each evaluation unit: supply exceeding demand, supply-demand balance, and supply falling short of demand. These matching states reflect the service flow logic between the service supply unit and the evaluation unit. Furthermore, based on the matching states, ArcGIS can be used to assign color values ​​to the matching states (supply exceeding demand, supply-demand balance, and supply falling short of demand) of each grid unit, generating a spatial distribution map of the supply-demand matching states, which visually presents the spatial pattern of the coupling results.

[0044] In this embodiment, as Figure 2 As shown, the high-value areas of potential ecosystem service supply in the city are concentrated in several areas surrounding large suburban parks and areas with a dense distribution of other types of parks within the urban built-up area. Conversely, the values ​​are generally lower in remote areas far from the city core (excluding the areas surrounding suburban parks). Similarly, the spatial distribution of ecosystem service demand endowment in the city is strongly coupled with population density and activity, exhibiting a "single-center polarization with multiple prominent nodes" pattern. Through analysis, it can be determined that the park recreation ecosystem services in the city exhibit the following characteristics and mechanisms: Figure 7 As shown, coupling the potential supply and visit volume of the assessment units reveals that the visit volume of almost all assessment units is less than the potential supply, indicating that the realized service value of the park is at a low level for almost all assessment units, suggesting that most of the potential supply has failed to translate into actual value; for example, Figure 8 As shown, coupling the potential demand and visit volume of the assessment units reveals that the visit volume to the park in almost all assessment units is lower than the potential demand, indicating that the potential demand for the park from the population of the corresponding assessment units is suppressed; for example Figure 9 As shown, coupling the potential supply and potential demand of the assessment units reveals a unique spatial pattern in the supply-demand matching of parks in the city: approximately 30% of the grid units (mainly located on the city's outskirts) have a lower potential demand than potential supply; conversely, the remaining 70% of the grid units concentrated in the city center show a lower potential supply than potential demand, indicating that the park supply in these assessment units cannot meet residents' needs. Overall, these analyses confirm that in the city's central area, the main constraint on improving the value of park recreational services lies on the supply side, while in the city's outskirts, it is primarily limited by the demand side.

[0045] In this embodiment, the target data also includes economic data and geographical data; the model building module also includes a regression module, which contains one or more independent regression functions. The regression functions are used to fit the population data, economic data, geographical data, potential supply and access volume of the evaluated unit, and select the optimal regression function as the regression analysis model of the regression module; the service flow analysis module is also used to call the regression analysis model to analyze the access demand of the evaluated unit to the service supply unit under different potential supply, population, economic and geographical conditions.

[0046] In this embodiment, the regression function includes the benchmark regression function: (5) Non-benchmark regression function: (6) (7) (8) In equations (5) to (8), The number of visits from the evaluated unit to the service provider unit. , These are the linear regression coefficients. As the dependent variable, Let be the ordinal number of the dependent variable. For regression coefficients, For linear regression error, , , , For the preset geographical weight matrix, For errors that do not have spatial autocorrelation, The regression coefficients are used; the dependent variables include population data, economic data, geographical data, and potential supply of the evaluated unit. In this embodiment, the regression function shown in equation (5) is the ordinary least squares regression function. Since the dependent variable is spatially distributed and has spatial autocorrelation, it may violate the independent and identically distributed assumption of the regression function. Therefore, based on the ordinary least squares regression function as the benchmark regression function, three other types of regression functions are constructed as non-benchmark regression functions, namely the spatial autoregressive function shown in equation (6), the spatial error regression function shown in equation (7), and the spatial autocorrelation combined regression function shown in equation (8). And from the goodness of fit ( The non-benchmark regression function is compared with the benchmark regression function through three dimensions: larger AIC / BIC, smaller AIC / BIC, and residual test (the closer Moran's l is to 0, the better). The regression function with the best overall performance is selected as the regression analysis model to eliminate the interference of model specification bias and ensure the accuracy of the model.

[0047] In this embodiment, the parameters of the regression function can be determined by fitting. The regression analysis model can be used to analyze the access volume of the corresponding evaluation unit to the service supply unit by setting dependent variables such as population data, economic data, geographical data, and potential supply. Furthermore, the supply and demand sides can be coupled to evaluate the impact relationship between socio-economic development and sensitive ecosystems.

[0048] The method for analyzing sensitive ecosystem service flows using multi-source spatiotemporal big data in this embodiment is a specific analysis method of the analysis system described above, and the implementation process is consistent with the specific embodiment of the analysis system described above. The method for analyzing sensitive ecosystem service flows using multi-source spatiotemporal big data in this embodiment includes the following steps: S1. Delineating geographic blocks based on a geographic information system, labeling service supply units and grid units, and constructing evaluation units based on the grid units; S2. Analyzing and processing the multi-source spatiotemporal big data of the geographic blocks to obtain target data; the target data includes population data and access volume; S3. Constructing one or more preset distance decay functions, and fitting the access volume and distance to the distance decay function based on the supply volume of the service supply unit, using the distance decay function with the best fit as the decay analysis model; S4. Calling the decay analysis model, performing spatial diffusion analysis based on the potential supply capacity of the service supply unit to determine the potential supply volume of the evaluation unit, and mapping the results to geographic blocks.

[0049] In this embodiment, the distance decay function includes: (1) (2) (3) (4) In equations (1) to (4), The number of visits from the evaluated unit to the service provider unit. , , , , , These are the fitting parameters for the distance decay function. The distance between the assessed unit and the service provision unit. For random variables, Indicates a normal distribution. The mean of the distances. denoted as the standard deviation of the distance.

[0050] In this embodiment, the target data also includes economic data and geographical data; step S3 further includes constructing one or more regression functions, fitting the regression functions according to the population data, economic data, geographical data, potential supply and access volume of the evaluation unit, and selecting the optimal regression function as the regression analysis model; step S4 further includes calling the regression analysis model to analyze the access demand of the evaluation unit to the service supply unit under different potential supply, population, economic and geographical conditions.

[0051] In this embodiment, the regression function includes the benchmark regression function: (5) Non-benchmark regression function: (6) (7) (8) In equations (5) to (8), The number of visits from the evaluated unit to the service provider unit. , These are the linear regression coefficients. As the dependent variable, Let be the ordinal number of the dependent variable. For regression coefficients, For linear regression error, , , , For the preset geographical weight matrix, For errors that do not have spatial autocorrelation, The regression coefficients are used; the dependent variables include population data, economic data, geographical data, and potential supply of the unit being evaluated.

[0052] In this embodiment, the resolution of the grid cells is less than a preset resolution threshold; the grid cells are combined into evaluation cells according to geographical boundaries and / or distance.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A sensitive ecosystem service flow analysis system based on multi-source spatiotemporal big data, characterized in that: It includes a geographic information module, a data processing module, a model building module, and a service flow analysis module; The geographic information module is used to delineate geographic blocks and display geographic region attributes. The geographic block includes service provisioning units and grid units, and an evaluation unit is constructed based on the grid units. The data processing module is used to analyze and process the multi-source spatiotemporal big data of the geographic block to obtain target data; the target data includes population data and access volume. The model building module includes a distance decay module, which contains one or more preset distance decay functions. The distance decay function fits the access volume and distance based on the supply volume of the service supply unit, and selects the distance decay function with the best fit as the decay analysis model of the distance decay module. The service flow analysis module is used to call the attenuation analysis model, perform spatial diffusion analysis based on the potential supply capacity of the service supply unit to determine the potential supply of the evaluation unit, and map the results to the geographic block.

2. The sensitive ecosystem service flow analysis system for multi-source spatiotemporal big data according to claim 1, characterized in that: The distance decay function of the model building module includes: （1） （2） （3） （4） In equations (1) to (4), The number of visits from the evaluated unit to the service provider unit. , , , , , These are the fitting parameters for the distance decay function. The distance between the evaluated unit and the service provision unit. For random variables, Indicates a normal distribution. The mean of the distances. denoted as the standard deviation of the distance.

3. The sensitive ecosystem service flow analysis system for multi-source spatiotemporal big data according to claim 2, characterized in that: The target data also includes economic data and geographical data; The model building module also includes a regression module, which contains one or more independent regression functions. The regression functions are used to fit the population data, economic data, geographical data, potential supply and access volume of the evaluated unit, and select the optimal regression function as the regression analysis model of the regression module. The service flow analysis module is also used to call the regression analysis model to analyze the access demand of the evaluation unit to the service supply unit under different potential supply, population, economic and geographical conditions.

4. The sensitive ecosystem service flow analysis system for multi-source spatiotemporal big data according to claim 3, characterized in that: The regression function includes the benchmark regression function: （5） Non-benchmark regression function: （6） （7） （8） In equations (5) to (8), The number of visits from the evaluated unit to the service provider unit. , These are the linear regression coefficients. As the dependent variable, Let be the ordinal number of the dependent variable. For regression coefficients, For linear regression error, , , , For the preset geographical weight matrix, For errors that do not have spatial autocorrelation, The regression coefficients are used; the dependent variables include population data, economic data, geographical data, and potential supply of the unit being evaluated.

5. The sensitive ecosystem service flow analysis system for multi-source spatiotemporal big data according to claim 4, characterized in that: The resolution of the grid cell is less than a preset resolution threshold; the geographic information module is also used to combine the grid cells into evaluation units based on geographic boundaries and / or distance.

6. A method for sensitive ecosystem service flow analysis using multi-source spatiotemporal big data, characterized in that... Includes the following steps: S1. Geographic blocks are delineated based on the geographic information system, service supply units and grid units are identified, and evaluation units are constructed based on the grid units; S2. Analyze and process the multi-source spatiotemporal big data of the geographic block to obtain target data; the target data includes population data and access volume; S3. Construct one or more preset distance decay functions, and fit the access volume and distance to the distance decay function based on the supply volume of the service supply unit, and use the distance decay function with the best fit as the decay analysis model. S4. Invoke the attenuation analysis model, perform spatial diffusion analysis based on the potential supply capacity of the service supply unit to determine the potential supply of the evaluation unit, and map the results to the geographic block.

7. The method for sensitive ecosystem service flow analysis based on multi-source spatiotemporal big data according to claim 6, characterized in that: The distance attenuation function includes: （1） （2） （3） （4） In equations (1) to (4), The number of visits from the evaluated unit to the service provider unit. , , , , , These are the fitting parameters for the distance decay function. The distance between the evaluated unit and the service provision unit. For random variables, Indicates a normal distribution. The mean of the distances. denoted as the standard deviation of the distance.

8. The method for sensitive ecosystem service flow analysis based on multi-source spatiotemporal big data according to claim 7, characterized in that: The target data also includes economic data and geographical data; Step S3 also includes constructing one or more regression functions, fitting the regression functions based on the population data, economic data, geographical data, potential supply and access volume of the evaluation unit, and selecting the optimal regression function as the regression analysis model. Step S4 also includes calling the regression analysis model to analyze the access demand of the assessment unit to the service supply unit under different potential supply, population, economic and geographical conditions.

9. The method for sensitive ecosystem service flow analysis based on multi-source spatiotemporal big data according to claim 8, characterized in that: The regression function includes the benchmark regression function: （5） Non-benchmark regression function: （6） （7） （8） In equations (5) to (8), The number of visits from the evaluated unit to the service provider unit. , These are the linear regression coefficients. As the dependent variable, Let be the ordinal number of the dependent variable. For regression coefficients, For linear regression error, , , , For the preset geographical weight matrix, For errors that do not have spatial autocorrelation, The regression coefficients are used; the dependent variables include population data, economic data, geographical data, and potential supply of the unit being evaluated.

10. The method for sensitive ecosystem service flow analysis based on multi-source spatiotemporal big data according to claim 9, characterized in that: The resolution of the grid cells is less than a preset resolution threshold; the grid cells are combined into evaluation cells based on geographical boundaries and / or distance.

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