A Spatial Data Processing and Demonstration Method for Analyzing the Compliance of Transportation National Land Planning

Abstract

The present invention discloses a method for spatial data processing and demonstration of traffic and national land planning compliance analysis, belonging to the technical field of data processing, and comprising the following steps: performing spatial buffer analysis on traffic planning data and national land planning data to obtain buffer zone data; performing spatial element intersection analysis on the buffer zone data and the national land planning data to obtain compliance analysis data; eliminating element data smaller than the gross error threshold; performing statistical analysis on the compliance analysis data through data fusion and data statistics; displaying traffic and national land planning spatial data, compliance analysis data, statistical charts and reports in map layouts at different levels, and configuring a multi-level associated compliance analysis display model; and displaying compliance analysis data and statistical data at different levels by clicking on different elements in the map layout. The data processing method of the present invention can be applied to the spatial compliance analysis of traffic planning and national land planning, and provides a scientific basis for planning compilation and adjustment.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of data processing, and particularly relates to a spatial data processing and demonstration method for traffic and territorial planning compliance analysis. Background Art

[0002] Territorial spatial planning is a guide for national spatial development, a spatial blueprint for sustainable development, and a basic basis for various development, protection, and construction activities. The territorial spatial planning requires that all special plans be coordinated with it. As an important part of the territorial spatial planning system, the transformation and upgrading of the planning compilation technical method is imperative.

[0003] The Digital Transportation Development Planning Outline requires the construction of a decision-making and planning system supported by big data, promotes the integration of multi-source data among departments and between governments and enterprises, and improves the level of transportation decision-making analysis. The Law on Environmental Impact Assessment of Plans and the General Guidelines for Technical Guidelines on Environmental Impact Assessment of Plans require carrying out planning coordination analysis and analyzing the compliance of the planning spatial layout with relevant plans such as territorial spatial planning, nature reserve planning, "Three Lines and One List", and important ecological sensitive targets.

[0004] Spatial compliance analysis is essentially spatial overlay analysis. Spatial overlay analysis is to overlay two or more element data of the same area and the same scale to generate a new data layer, so that each element of the new data layer has the multiple attributes of the elements of each overlay layer or the statistical characteristics of the attributes of the elements of each overlay layer. Overlay analysis not only includes the comparison of spatial data relationships but also includes the comparison of attribute data relationships.

[0005] Existing spatial overlay analysis models and tools have problems such as diverse data source types, complex operations, and weak industry pertinence, and cannot well solve the application problems in the actual business field. In particular, the spatial compliance analysis related to traffic planning research is still blank and is not sufficient to support the traffic planning research work under the background of territorial spatial planning.

[0006] In the design of existing data visualization libraries, the types of geographical data visualization forms are few. For complex geographical data, the chart types cannot meet the data requirements, or cannot fully display the characteristics of the data and reveal the laws of the data. In some visualization platforms and tools, there are problems with the visualization cost of obtaining map data, or only a limited number of visualization templates are provided, which cannot meet the expression needs of diverse geographical data. Currently, there is no visualization performance tool in the visualization tools that can realize spatial overlay analysis, and there are problems with poor associated display and interaction capabilities of spatial elements. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the purpose of the present disclosure is to provide a data processing method for traffic and territorial planning compliance analysis, which solves the problems in the prior art that there is no visualization tool capable of realizing spatial overlay analysis in visualization tools, and there are still gaps in the associated display and interaction capabilities of spatial elements.

[0008] Regarding the problems of the prior art.

[0009] The purpose of the present disclosure can be achieved through the following technical solutions:

[0010] A spatial data processing method for traffic and territorial planning compliance analysis, the method comprising the following steps:

[0011] Step 1: Preprocessing of traffic and territorial planning spatial data:

[0012] The sources of the traffic and territorial planning spatial data in this step include vector data, raster data, and image data obtained from planning schemes such as traffic planning and territorial planning. These three types of data are the input data content of this method. Through data preprocessing, standardized traffic and territorial planning spatial analysis data is output. The processing process is as follows: (1) Preprocessing of raster data vectorization, spatial registration and vectorization preprocessing of image data; (2) Performing spatial information standardization preprocessing on the processed vectorized data together with the original vector format data, including coordinate transformation, uniformly converting to the CGCS2000 coordinate system, and checking the data spatial topology structure, such as road network connectivity processing, dangling point processing, etc.; (3) Performing attribute data structure standardization preprocessing on the spatially standardized data. The attribute data structure of traffic planning spatial data includes fields such as data type, planning type, planning scope, planning horizontal year, traffic element name, technical grade, construction status, planned mileage or planned area, etc. The data structure of territorial spatial planning contains fields such as data type, land use type, land use code, located unit, patch area, etc.; (4) Finally, outputting standardized traffic and territorial planning spatial analysis data.

[0013] Step 2: Traffic and territorial planning compliance analysis:

[0014] The input data for this step is mainly the preprocessed traffic and land use planning spatial analysis data obtained in Step 1. For the preprocessed traffic planning spatial analysis data (excluding land use planning spatial analysis data) in Step 1, data classification is performed according to the mapping relationship model between planning indicators and buffer distances, so as to determine whether data buffering is required and the data buffer distance. Then, the spatial element intersection analysis is performed on the traffic planning spatial analysis data after buffer analysis and the preprocessed land use planning spatial data to obtain the compliance analysis data. The processing process is as follows: (1) According to the data types of spatial data points, lines, and polygons, combined with the planning types of traffic planning, a mapping relationship model between planning indicators and buffer distances is established, that is, the traffic planning analysis data is mapped to the planning buffer distance according to two planning indicators: planning type and planning technical level; (2) If the data type of the preprocessed traffic planning spatial analysis data in Step 1 is point or line element data, spatial buffer analysis is performed according to the mapping relationship model between planning indicators and buffer distances. The line data elements are buffered into rectangular polygon elements, and the point data elements are buffered into circular polygon elements. A buffer distance field (buffdist) is added to the data, and the buffer distance values corresponding to the data elements with different planning indicators are used to assign values to this field; (3) Then, the spatial element intersection analysis is performed on the traffic planning spatial analysis data after buffer analysis and the preprocessed land use planning spatial data to obtain the intersection of the geometric shapes of the two planning elements. Only the intersecting part of the two planning geometric shapes is retained as the geometric shape of the output data; the input traffic planning spatial data and land use planning spatial data are associated and transferred to the output data as the attribute table of the output data, so as to obtain the compliance analysis data.

[0015] Step 3: Governance of compliance analysis data:

[0016] This step mainly performs result data governance on the compliance analysis data obtained in Step 2, screens out the data outliers caused by data vectorization and spatial buffer analysis, and eliminates the element data smaller than the data outlier threshold. The processing process is as follows:

[0017] (1) Calculate the area and length of the elements.

[0018] Calculate the geometric area (area) of the compliance analysis polygon elements obtained in Step 2, and add an area (area) field to the data attribute table; if the data type is line element, such as the planning type is highway planning, the length (length) of the line element also needs to be calculated using the buffer distance (buffdist) in Step 2, and a length (length) field is added to the data attribute table. The length (length) formula is length = area / buffdist.

[0019] (2) Calculate the gross error threshold. Establish the connection between data governance and user usage. Through human-computer interaction for error correction, the user views the compliance analysis data for which the length and area have been calculated. If obvious intersecting gross error data elements are found in areas such as road network intersection sections and hub port surface boundaries, the length and area of the measured elements are measured in real time, and the maximum value of the measured values is taken as the gross error threshold.

[0020] (3) Screen spatial data using the gross error threshold. Use the gross error threshold corrected by the user as the input condition to screen the compliance analysis data elements in the length and area fields that are less than the gross error threshold. Secondary human-computer interaction error correction can be performed to remove non-gross error data elements from the screening results.

[0021] (4) Eliminate the screened data gross error elements. Delete the compliance analysis data elements with gross errors screened out, and finally obtain the compliance analysis data after data governance.

[0022] Step 4: Statistical analysis method for compliance analysis data:

[0023] Use the compliance analysis data obtained in Step 3 to perform data fusion according to the traffic planning index fields and land use planning index fields to obtain the fused compliance analysis data. Obtain the compliance analysis data statistical chart according to the statistical dimension of the planning compliance data, and obtain the compliance analysis data statistical report according to the data statistical report template. The processing process is as follows: (1) Select the traffic planning index fields and land use planning index fields from the compliance analysis data after data governance. The field selection range is the fields standardized in the preprocessing data structure in Step 1, including optional planning types, planning scopes, planning horizon years, traffic element names, technical grades, construction status in traffic planning, and land use types, land use codes, located units, patch areas, etc. that can be selected in the land use spatial planning fields;

[0024] (2) Perform data fusion analysis according to the selected index fields. Use the selected planning compliance data fields in step (1) as the data fusion feature fields. One is to perform spatial primitive element fusion, that is, fuse the primitives with the same values in the selected fields. The other is to perform attribute data fusion, that is, merge the data with the same values in the selected fields in the attribute data structure table. If the data type is character type, take the unique value. If it is numerical type, calculate the maximum value, minimum value, average value or sum. The sum statistics need to be performed on the length and area fields calculated in Step 3, and other fields in the data attribute table that are not selected fields are deleted;

[0025] (3) Extract the data attribute table after data fusion as the planning compliance data statistical table;

[0026] (4) Use the data statistical table to draw a data statistical chart. The statistical chart is generally selected as a two-dimensional bar chart. The horizontal coordinate of the chart is the index field selected in step (1), and the vertical coordinate is the length or area of the data fusion statistics in step (2); (5) Use the data statistical table to produce a compliance analysis data statistical report. According to the spatial compliance analysis data statistical table, further summarize the conflict crossing length or occupied area between the transportation plan and the territorial space plan, and screen out the key information required in the statistical report from the input data in step 2 to obtain a written report.

[0027] Step 5: Multi-level associated compliance analysis display model:

[0028] In this step, the compliance analysis data is configured with a data visualization display model. The preprocessed transportation and territorial space data, the space compliance analysis data after data governance, the compliance analysis statistical chart and report are used to display the results of the transportation and territorial space compliance analysis data through the configuration of data association links, and a multi-level associated compliance analysis display model is established to improve the visualization level of the results.

[0029] The specific configuration steps are as follows:

[0030] (1) Input the preprocessed transportation and territorial planning space analysis data in step 1 into the map layout. Through the GIS symbolization tool, configure the map style according to the planning symbols of transportation and territory, and use it as the first-level planning space data display map. Among them, the territorial planning space data is displayed as the base map, such as the highway network planning space layout data;

[0031] (2) Link the compliance analysis data after data governance in step 3 to the spatial graphic elements and attribute data elements of the first-level planning space data display map, and input it into the secondary compliance analysis data map layout as the secondary compliance analysis data display content. This step can link multiple compliance analysis data processed through steps 1, 2, and 3. For example, link the compliance analysis data between the highway network planning and the basic farmland protection area, the compliance analysis data between the highway network planning and the ecological red line, and the compliance analysis data between the highway network planning and the urban protection area to one or more graphic elements and attribute elements in the highway network planning layout map;

[0032] (3) Link the compliance analysis statistical chart and report in step 4 to the secondary compliance analysis data map layout as the tertiary compliance analysis statistical data display content. Each secondary compliance analysis data in this step can be linked to multiple statistical charts and statistical reports. For example, in the compliance analysis data between the highway network planning and the basic farmland protection area, link the statistical charts and statistical reports of the non-compliant patch areas of different highway routes and the basic farmland protection area.

[0033] (4) Complete the configuration of the multi-level associated compliance analysis display model and output the display model.

[0034] Step 6: Visualization demonstration of traffic and territorial planning compliance analysis data:

[0035] Perform a visualization demonstration on the multi-level associated compliance analysis display model output in Step 5. The specific display steps are as follows:

[0036] (1) First, open the model. The first map layout page displays the traffic planning spatial data display map configured in Step 5-(1);

[0037] (2) By clicking on the traffic planning spatial data graphic elements that are linked to the compliance analysis data in Step 5-(2), the entire compliance analysis data display map directory configured in Step 5-(2) is displayed on the second map layout page;

[0038] (3) Through any secondary compliance analysis data display map in the compliance analysis data display map directory, enter the third map layout page to display the enlarged compliance analysis data display map, and simultaneously display the tertiary compliance analysis statistical data configured in Step 5-(3).

[0039] Advantages of the present disclosure:

[0040] The present invention uses the control relationship model between planning indicators and buffer distances to perform spatial buffer analysis on traffic planning data, expands point and line elements into surface elements, takes into account the spatial influence range of traffic planning and the control requirements of territorial spatial planning, and improves the rationality and reliability of compliance analysis. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is the flowchart of the traffic and territorial planning compliance analysis data processing and result demonstration method in this embodiment;

[0043] Figure 2 It is the flowchart of the traffic and territorial planning spatial data standardization preprocessing in this embodiment;

[0044] Figure 3 It is the flowchart of the traffic and territorial planning compliance analysis in this embodiment;

[0045] Figure 4 It is the flowchart of the compliance analysis data governance in this embodiment;

[0046] Figure 5 is the flowchart of statistical analysis of compliance analysis data in this embodiment;

[0047] Figure 6 is the flowchart of configuring the display model for multi-level associated compliance analysis in this embodiment;

[0048] Figure 7 is the flowchart of visual demonstration of compliance analysis data for transportation and national land planning in this embodiment;

[0049] Figure 8 is the compliance analysis map of highway national land space planning with line feature data type in this embodiment;

[0050] Figure 9 is the compliance analysis map of transportation hub national land space planning with point and surface feature data types in this embodiment. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of the present disclosure will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

[0052] Embodiment 1: As Figure 1 shown, a spatial data processing method for compliance analysis of transportation and national land planning includes the following steps

[0053] Step 1: Preprocessing of spatial data for transportation and national land planning:

[0054] Planning compliance analysis refers to the analysis of the spatial position relationship between the transportation special plan and the upper-level spatial constraint plan (here refers to the national land space plan). Through the result data of the compliance analysis, it can be obtained whether the spatial layout of the transportation special plan meets the spatial control requirements of the upper-level plan, and the specific spatial position data with contradictions or conflicts can be clarified.

[0055] For the input data requirements of the spatial compliance analysis of the two types of plans, the main sources of the compliance analysis data for transportation and national land planning include the data obtained from the planning schemes such as transportation planning and national land planning. Specifically, it is divided into planning vector data, planning raster data, and planning picture data. These three types of data are the input data content of this method. The main content of this step is to output the input vector data, raster data, and picture data of transportation and national land planning as standardized spatial analysis data for transportation and national land planning through data preprocessing.

[0056] As Figure 2 shown, the data processing steps are:

[0057] (1.1) Preprocessing for raster data vectorization, spatial registration and vectorization preprocessing of image data. Use the raster-vector conversion tool in GIS software to convert raster data representing planning element information in raster cells into vector data representing planning element information with points, lines, and polygons according to the algorithm of raster corner coordinates. For image data, use the spatial registration tool in GIS software to select graphic feature points of the image for spatial registration, and use point, line, and polygon editing tools to draw vector data representing planning element information with points, lines, and polygons.

[0058] (1.2) Perform preprocessing for spatial information standardization on the processed vectorized data together with the original vector format data, which is the process of unifying multi-source spatial data. Specifically, it includes coordinate conversion, such as uniformly converting to the CGCS2000 coordinate system, and unifying the spatial reference benchmark through coordinate conversion. Because the data processing requirements for spatial compliance analysis require that vector data have the same spatial reference benchmark, that is, the data participating in the compliance analysis requires that the plane coordinate system and the projection coordinate system be consistent; check the spatial topology structure of the data, such as road network connectivity processing and dangling point processing. It is very necessary to check and correct the road section connectivity and dangling points of nodes in traffic planning road network data. Further eliminate vectorization errors in the data through spatial topology structure inspection.

[0059] (1.3) Perform preprocessing for standardizing the attribute data structure of the spatially standardized data. The attribute data structure of traffic planning spatial data includes fields such as planning type, planning scope, planning horizontal year, traffic element name, technical grade, construction status, planned mileage (area), etc. The data structure of territorial spatial planning data includes fields such as land use type, land use code, located unit, and patch area. For the subsequent data governance, data statistical analysis, and standardized management of data storage after spatial compliance analysis, it is necessary to further standardize the data structure of the fields of spatial data, and construct a standardized data structure for input data through preprocessing.

[0060] (1.4) Finally, output standardized traffic and territorial planning spatial analysis data.

[0061] Step 2: Spatial overlay analysis for planning compliance analysis:

[0062] Taking road network planning, hub planning, and port planning as typical representatives, sort out the requirements for planning compliance analysis. Based on different spatial elements of traffic special planning and the spatial constraint characteristics of territorial spatial planning, study the design rules for buffer distances of different types of traffic elements, and conduct research on spatial overlay analysis algorithms with point, line, and polygon spatial data types to identify the intersection relationships between elements and obtain traffic-territorial spatial compliance analysis data.

[0063] Such asFigure 3 As shown in the figure, the steps of data compliance analysis are as follows:

[0064] (2.1) Establish a relationship model between planning indicators and buffer distances. According to the data types of spatial data points, lines, and surfaces, combined with two planning indicators, namely the planning type and the planning technology level of traffic planning, establish a control relationship model between planning indicators and planning buffer distances.

[0065] Due to the macroscopic nature of traffic planning, it is generally impossible to deepen to the specific route selection and site selection of planning elements. There will be a certain line position swing in the actual line position planning stage and the feasibility study stage of line projects of the planned road network. In the detailed planning stage, the planned hub will further conduct land and sea use evaluation and refinement. Therefore, in the planning analysis stage, during the compliance analysis process of traffic planning such as the planned road network and hub with the national territorial space planning, it is necessary to conduct a certain swing study on the line network and hub points, expand the road network lines and hub points to traffic land, that is, expand the spatial line and point element layers to spatial surface element layers.

[0066] Table 1 Sample table of the control relationship between traffic planning indicators and buffer distances

[0067]

[0068] (2.2) If it is determined that the data types of the traffic planning spatial analysis data preprocessed in step (2.1) are point and line element data, then according to the control relationship model between planning indicators and buffer distances, conduct spatial buffer analysis. Road network planning, waterway layout planning, and shoreline utilization planning are typical line element type traffic special plans. Buffer the line data elements into rectangular surface elements; the layout planning of ports, passenger and freight hubs, and airports are typical point element type traffic special plans. Buffer such point data elements into circular surface elements; the overall port planning is a typical traffic special plan at the micro scale of surface elements, and its spatial position is relatively accurate, and generally there is no need to conduct buffer analysis on it; and add a buffer distance field (buffdist) to the buffer analysis data table, and assign the buffer distance values corresponding to the data elements of different planning indicators to this field.

[0069] (2.3) Then conduct an intersection analysis of spatial elements between the traffic planning spatial analysis data after buffer analysis processing and the preprocessed national land planning spatial data. According to the intersection method of surface elements and surface elements, obtain the intersection of the geometric figures of the two planning elements, and only retain the intersecting part of the two planning geometric figures as the geometric figure of the output data; transfer the association between the input traffic planning spatial analysis data and the national land planning spatial data to the output data as the attribute table of the output data, so as to obtain compliance analysis data.

[0070] Step Three: Governance of compliance analysis data:

[0071] The type of traffic and land compliance analysis data is surface elements. Due to factors such as the vectorization accuracy of the planning element graphic coordinates and the buffer analysis settings of the traffic planning spatial data, there are errors in the compliance analysis data. In order to eliminate the gross errors in the compliance analysis results caused by such factors in this study, by calculating the thresholds based on area or length, setting filtering conditions, filtering out the patches or sections below the thresholds in the overlay results, the compliance analysis data after data governance is obtained.

[0072] As Figure 4 shown, the specific data governance steps are as follows:

[0073] (3.1) Calculate the area and length of the elements.

[0074] Calculate the geometric area (area) of the compliance analysis surface elements, add an area (area) field in the data attribute table. Specifically, through GIS tools, the spatial polygon surface elements of the compliance analysis results are calculated by trapezoidal projection according to the polygon area with known vertex coordinates.

[0075] If the data type is line elements, such as the planning type is highway planning, the buffer distance (buffdist) field value in the spatial buffer analysis is also required. Calculate the length (length) of the line elements, and add a length (length) field in the data attribute table. The length (length) calculation formula is length = area / buffdist.

[0076] (3.2) Calculate the gross error threshold. Establish the connection between data governance and user use. Through human-computer interaction for error correction, users view the compliance analysis data for which the length and area have been calculated. If there are obvious intersecting gross error data elements found in areas such as road network intersection sections and hub port surface boundaries, measure the length and area of the elements in real time, and take the maximum value of the measured values as the gross error threshold.

[0077] (3.3) Use the gross error threshold to screen the spatial data. Use the gross error threshold corrected by the user as the input condition to screen the compliance analysis data elements in the data attribute table whose length (length) and area (area) fields are less than the gross error threshold. Synchronously view the corresponding geometric graphics, and then perform secondary human-computer interaction for error correction to remove the non-gross error data elements in the screening results.

[0078] (3.4) Eliminate the screened data gross error elements. Delete the compliance analysis data elements with gross errors screened out, and finally obtain the compliance analysis data after data governance.

[0079] Step Four: Compliance Analysis Data Statistical Analysis Model:

[0080] According to the compliance analysis data and the requirements of the spatial compliance analysis business scenario, calculate multi-dimensional compliance analysis statistical indicators, and successively perform data fusion processing, data statistical analysis, and the production of statistical charts and reports to support the configuration of visualization tools for planning analysis and subsequent result data.

[0081] As Figure 5 shown, the specific processing process is as follows:

[0082] (4.1) Select data fusion fields. Select traffic planning index fields and land use planning index fields from the compliance analysis data after data governance. The field selection range is the fields after the standardization of the preprocessed data structure, including optional planning types, planning scopes, planning horizon years, traffic element names, technical grades, construction status of traffic planning, and land use types, land use codes, located units, patch areas, etc. that can be selected for land use planning fields;

[0083] (4.2) Conduct data fusion analysis according to the selected fields. Take the planning compliance data fields selected in step (4.1) as data fusion feature fields, merge two or more graphic elements with the same value in the selected fields into one graphic element, perform spatial graphic element fusion, and synchronously merge two or more attribute data corresponding to the graphic fusion. If the data type of the selected field is character type, take the unique value; if it is numerical type, calculate the maximum value, minimum value, average value or sum. Among them, the length and area fields calculated in step three are summed, and other fields in the data attribute table that are not selected fields are deleted;

[0084] (4.3) Extract the data attribute table after data fusion as a planning compliance data statistical table;

[0085] For example, in the compliance analysis of highway land use planning, first, it is necessary to statistically count, for each traffic planning project dimension, the control areas in the land use planning that have layout conflicts with each traffic planning project, as well as the involved length or area; second, it is necessary to statistically count, for each land use planning control area dimension, the traffic planning projects that have layout conflicts with the control areas in the land use planning, as well as the involved length or area, so as to obtain a planning compliance data statistical table to support the generation of statistical charts and reports.

[0086] Table 2 Statistical Example Table 1 of Planning Compliance Analysis (According to Traffic Planning Project Dimension)

[0087]

[0088] Table 3 Statistical Example Table 2 of Planning Compliance Analysis (According to Land Use Planning Control Area Dimension)

[0089]

[0090] (4.4) Use the data statistical table to draw a data statistical chart. The statistical chart is generally selected as a two-dimensional bar chart, where the horizontal coordinate of the chart is the selected index field, specifically the two dimensions of the data statistical table. One is the traffic planning project dimension, mainly the route code or route name, and the other is the territorial space planning control area dimension, mainly the control area name or functional zoning; the vertical coordinate of the statistical chart is the length or area of the data fusion statistics.

[0091] (4.5) Use the data statistical table to produce a compliance analysis data statistical report.

[0092] According to the spatial compliance analysis data statistical table, further summarize the crossing length or occupied area of the conflict areas between the traffic planning and the territorial space planning, and screen out the key information required in the statistical report from the input data in step two to obtain a text report. Provide two statistical report templates according to the planning type. One is the statistical template for the line element data type, such as the territorial space compliance analysis of highways, and the main statistical index is the involved mileage; the other is the statistical template for the point and surface element data types, such as the territorial space compliance analysis of hubs and ports, and the main statistical index is the occupied area.

[0093] Taking the territorial space planning compliance analysis of highways of the line element data type as an example, the example of the text report is Figure 8 .

[0094] Taking the territorial space planning compliance analysis of traffic hubs of the point and surface element data type as an example, the example of the text report is Figure 9 .

[0095] Step Five: Configuration of the multi-level associated compliance analysis display model:

[0096] Through data association model configuration, display the traffic territorial space compliance analysis data results with the preprocessed traffic territorial space data, the spatially compliant analysis data after data governance, the compliance analysis statistical charts and reports, establish a multi-level associated compliance analysis display model, and improve the visualization level of the results.

[0097] As Figure 6 shown, the specific model configuration method is:

[0098] Perform spatio-temporal data visualization display on the compliance analysis data, overlay the preprocessed traffic and territorial planning spatial analysis data in step one on the map, display the compliance analysis data after data governance in step three from the spatial dimension, display the situation of the traffic planning spatial layout data conforming to and not conforming to the territorial space planning data from the perspective of the traffic territorial compliance analysis business, and finally hang the statistical charts and statistical reports obtained in step four on the map to effectively display the data analysis and statistics situation. The three-level visualization map layout can be associated and interactively clicked to view. The specific steps are:

[0099] (5.1) Input the traffic and land use planning spatial analysis data pre - processed in Step 1 into the map layout. Through the GIS symbolization tool, configure the map style according to the planning symbols of traffic and land use, and use it as the first - level planning spatial data display map. Among them, the land use planning spatial data is displayed as the base map, such as the highway network planning spatial layout data;

[0100] (5.2) Link the compliance analysis data after data governance in Step 3 to the spatial graphic elements and attribute data elements of the first - level planning spatial data display map, and input it into the second - level compliance analysis data map layout as the content of the second - level compliance analysis data. This step can link multiple compliance analysis data processed through Steps 1, 2, and 3. For example, link the compliance analysis data of highway network planning and basic farmland protection area, the compliance analysis data of highway network planning and ecological red line, and the compliance analysis data of highway network planning and urban protection area to one or more graphic elements and attribute elements in the highway network planning layout map;

[0101] (5.3) Link the compliance analysis statistical charts and statistical reports in Step 4 to the second - level compliance analysis data map layout as the content of the third - level compliance analysis statistical data. Each second - level compliance analysis data in this step can be linked to multiple statistical charts and statistical reports. For example, in the compliance analysis data of highway network planning and basic farmland protection area, link the statistical charts and statistical reports of the non - compliance patch areas of different highway routes and basic farmland protection areas.

[0102] (5.4) Complete the configuration of the multi - level associated compliance analysis display model and output the display model.

[0103] Step 6: Visualization of traffic and land use planning compliance analysis data:

[0104] Visualize the multi - level associated compliance analysis display model output in Step 5 and the models configured in Steps 1 - 3. The visualization model can illustrate the three - level display linkage relationship:

[0105] The first - level displays the traffic planning research area level. When selecting a geometric graphic of a certain spatial element, automatically calculate the relevant geometric elements with spatial topological relationship with this spatial graphic and enter the second - level area level;

[0106] The second - level display is to strip and display the relevant spatial elements of the selected area by layer to form a map directory, fully demonstrating the data overlay and association relationship;

[0107] The third - level display is to dynamically display the data such as the full planning scope, individual planning elements or small areas, and statistical analysis charts by retrieving the layered data of the second - level area.

[0108] The specific display steps are as follows:

[0109] Open the model. The first map layout page displays the traffic planning spatial data display map configured in Step 1. Click on the traffic planning spatial data graphic elements therein, and the second map layout page displays the entire map directory of the compliance analysis data configured in Step 2. Click on one of the secondary compliance analysis data display maps, and the third map layout page displays the magnified compliance analysis data display map, and simultaneously displays the tertiary compliance analysis statistical data configured in Step 3. The specific flowchart is as Figure 7 shown.

[0110] Example 2: As Figure 1 shown, the present invention relates to a data processing method for traffic and land planning compliance analysis, and its specific implementation manner is as follows:

[0111] S1: Through the control relationship model of planning indicators and buffer distances, perform spatial buffer analysis on traffic planning data and land planning data to obtain buffer zone data;

[0112] The control relationship model of planning indicators and buffer distances is established according to the control requirements of national land space planning and the spatial influence range of traffic planning indicators. For example, for highway planning, different buffer distances are set according to its technical grade and construction status. Spatial buffer analysis is to use the spatial analysis tool of GIS software to expand the point and line elements in traffic planning data into surface elements according to the corresponding buffer distances to obtain buffer zone data.

[0113] S2: Perform spatial element intersection analysis on the buffer zone data and land planning data to obtain compliance analysis data;

[0114] Spatial element intersection analysis is to use the spatial analysis tool of GIS software to perform spatial overlay operation on the buffer zone data and land planning data, and obtain the intersection part of the two data as the compliance analysis data. The attribute table of the compliance analysis data contains relevant fields of traffic planning data and land planning data, such as planning type, planning scope, planning horizontal year, traffic element name, technical grade, construction status, land use type, land use code, located unit, patch area, etc.

[0115] S3: Through human-computer interaction error correction, perform data governance on the compliance analysis data to eliminate the element data smaller than the gross error threshold;

[0116] Data governance is to eliminate the errors in the compliance analysis data caused by factors such as data vectorization accuracy and buffer distance setting. By calculating the threshold based on area or length, set the filtering conditions to filter out the patches or sections below the threshold in the overlay results to obtain the compliance analysis data after data governance. The process of data governance includes the following steps:

[0117] S31: Calculate the area or length of the compliance analysis data and add corresponding fields in the attribute table;

[0118] S32: Check the compliance analysis data through human-computer interaction, measure the area or length of obvious error elements, and take the maximum value as the gross error threshold;

[0119] S33: Use the gross error threshold to filter the conformity analysis data and remove the element data that is smaller than the threshold.

[0120] S4: Conduct statistical analysis on the compliance analysis data through data fusion and data statistics to obtain compliance analysis data statistical tables, statistical charts and statistical reports;

[0121] Data statistics are used to reflect the spatial conformity of transportation planning and land planning. According to different statistical dimensions and statistical indicators, data fusion and data statistics are performed on the conformity analysis data to obtain conformity analysis data statistical tables, statistical charts and statistical reports. The data statistics process includes the following steps:

[0122] S41: Select data fusion fields, such as traffic planning type, planning scope, planning level year, traffic element name, technical level, construction status, land use type, land use code, location unit, etc.;

[0123] S42: Perform data fusion analysis according to the selected fields, merge the elements with the same field value into one element, and perform sum statistics on the area or length field to obtain the data after data fusion;

[0124] S43: extracting the data attribute table after data fusion into a compliance analysis data statistics table;

[0125] S44: Use the data statistics table to draw data statistics charts, such as bar charts, pie charts, etc., to show the distribution of compliance analysis data in different dimensions;

[0126] S45: Use statistical tables to prepare compliance analysis data statistical reports, summarize the spatial conflicts between transportation planning and land planning, and provide corresponding suggestions and measures.

[0127] S5: Through data association links, the spatial data of transportation and land planning, compliance analysis data, statistical charts and reports are displayed in map layouts at different levels, and a multi-level association compliance analysis display model is configured;

[0128] The data association link is for realizing the dynamic display of the data for the compliance analysis of transportation and land use planning. By setting data association rules, the spatial data of transportation and land use planning, compliance analysis data, statistical charts, and reports are displayed in map layouts at different levels according to different data levels and data types, forming a multi-level associated compliance analysis display model. The process of the data association link includes the following steps:

[0129] S51: Set data association rules, such as associating data according to fields such as planning type, planning scope, planning horizontal year, transportation element name, technical grade, construction status, land use type, land use code, and located unit;

[0130] S52: Display the spatial data of transportation and land use planning, compliance analysis data, statistical charts, and reports in map layouts at different levels according to different data levels and data types, such as national level, provincial level, municipal level, district and county level, etc., to form a multi-level associated compliance analysis display model;

[0131] S53: By clicking on different elements in the map layout, trigger the data association rules to display the compliance analysis data and statistical data at different levels related to this element, and realize the dynamic display of the data for the compliance analysis of transportation and land use planning.

[0132] The data processing method of the present invention can be applied to the spatial compliance analysis of transportation planning and land use planning, improve the analysis efficiency and accuracy, and provide a scientific basis for planning compilation and adjustment.

[0133] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0134] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A data processing method for traffic and territorial planning compliance analysis, characterized in that, The following steps are involved: S1: Standardize the spatial information and attribute data structure of the vector data, raster data, and image data obtained from the transportation planning and land planning schemes, and output standardized transportation and land planning spatial analysis data; S2: Classify the traffic planning spatial analysis data and conduct spatial buffer analysis according to the planning index and buffer distance comparison relationship model, and then conduct spatial element intersection analysis on the traffic planning spatial analysis data after buffer analysis and the land planning spatial data to obtain compliance analysis data; S3: Conduct data governance on the compliance analysis data, filter out the data errors caused by data vectorization and spatial buffer analysis, and remove the feature data that is less than the data error threshold; S4: Using the filtered compliance analysis data, data fusion is performed according to the traffic planning indicator field and the land planning indicator field to obtain the fused compliance analysis data, and a compliance analysis data statistical chart is obtained according to the planning compliance data statistical dimension, and a compliance analysis data statistical report is obtained according to the data statistical report template; S5: Configure the data visualization display model for the compliance analysis data, and display the transportation land space compliance analysis data results through the configuration of data association links, including the pre-processed transportation land space data, the spatial compliance analysis data after data governance, and the compliance analysis statistical charts and reports, and establish a multi-level association compliance analysis display model; The specific steps of S2 are as follows: According to the data types of spatial data points, lines and surfaces, combined with the two planning indicators of transportation planning, planning type and planning technology level, a comparison relationship model between planning indicators and buffer distance is established; According to the comparison relationship model between planning indicators and buffer distance, spatial buffer analysis is performed on point and line feature data, line data features are buffered into rectangular surface features, point data features are buffered into circular surface features, and a buffer distance field is added; Perform spatial element intersection analysis on the traffic planning spatial data after buffer analysis and the land planning spatial data, obtain the intersection of the two planning element geometries, retain the intersection part as the output data geometry and attribute table, and obtain the conformity analysis data; The specific steps of S3 are as follows: Use GIS tools to calculate the area of compliance analysis surface elements and the length of line elements, and add corresponding fields to the data attribute table; Through human-computer interactive correction, users check and measure the conformity analysis data. If data elements with obvious intersecting gross errors are found, the maximum value of the measured value is taken as the gross error threshold; Use the gross error threshold to filter spatial data, remove the conformity analysis data elements whose length and area fields are smaller than the gross error threshold, and then perform a second human-computer interaction correction; Eliminate the gross error elements in the screened data and obtain the compliance analysis data after data governance.

2. The data processing method for traffic and land planning compliance analysis according to claim 1, characterized in that The method also includes visually demonstrating the output multi-level association compliance analysis display model.

3. The data processing method for traffic and territorial planning compliance analysis according to claim 1, characterized in that The specific steps of S1 are as follows: The raster data is converted into vector data according to the algorithm of raster corner coordinates through GIS software, the image data is spatially registered and the vector data is drawn; Perform spatial information standardization preprocessing on the vectorized data and the original vector data, including coordinate transformation and data space topology structure checking; Perform attribute data structure standardization preprocessing on the spatially standardized data, and construct the data structure of the normalized input data; Output the standardized traffic and land use planning spatial analysis data.

4. A data processing method for traffic and territorial planning compliance analysis according to claim 1, characterized in that, The specific steps of S4 are as follows: Select the traffic planning index fields and land use planning index fields from the compliance analysis data after data governance as the data fusion fields; According to the data fusion fields, perform spatial and attribute fusion on the graphic elements and attribute data with the same value. If the selected field data type is character type, take the unique value. If it is numerical type, calculate the maximum value, minimum value, average value or sum, and delete the non-selected fields; Extract the data attribute table after data fusion as the planning compliance data statistical table, and count the length or area of the layout conflicts with each traffic planning project or control area according to the traffic planning project dimension and the land use planning control area dimension; Use the data statistical table to draw a data statistical chart. The statistical chart is a two-dimensional bar chart, with the selected index fields on the abscissa and the length or area of the data fusion statistics on the ordinate; Use the data statistical table to make a compliance analysis data statistical report, summarize the crossing length or occupied area of the conflict areas between the traffic planning and the land use planning, and screen out the key information from the input data in step two to obtain a text report.

5. A data processing method for traffic and territorial planning compliance analysis according to claim 1, characterized in that The specific steps of S5 are as follows: Input the preprocessed traffic and land use planning spatial data into the map layout, configure the map style according to the planning symbols, and use it as the first-level planning spatial data display map; Associate the compliance analysis data after data governance with the graphic elements and attribute data elements of the first-level planning spatial data display map as the second-level compliance analysis data display content, and multiple compliance analysis data can be associated; Associate the compliance analysis statistical chart and statistical report with the second-level compliance analysis data display map as the third-level compliance analysis statistical data display content. Each second-level compliance analysis data can be associated with multiple statistical charts and reports; Complete the configuration of the multi-level associated compliance analysis display model and output the display model.

6. The data processing method for traffic and territorial planning compliance analysis according to claim 2, characterized in that, The specific steps of the visual demonstration are as follows: Open the multi-level associated compliance analysis display model to display the preprocessed traffic and land use planning spatial data; Click on the graphic element of the traffic planning spatial data to display the related compliance analysis data as the map directory; Click on one item of the compliance analysis data in the map directory to display the enlarged compliance analysis data, and synchronously display the statistical chart and statistical report; Output the visual results.

7. A storage medium, characterized in that, The storage medium stores a program for executing a data processing method for traffic and land use planning compliance analysis according to any one of claims 1-6.

8. A data system for traffic and territorial planning compliance analysis, characterized in that, It includes the following modules: Data preprocessing module: Perform spatial information standardization and attribute data structure standardization on the vector data, raster data, and picture data obtained from the traffic planning and land use planning schemes, and output the standardized traffic and land use planning spatial analysis data; Conformance Analysis Module: Classify the traffic planning spatial analysis data according to the mapping relationship model between planning indicators and buffer distances, and conduct spatial buffer analysis. Then, perform spatial element intersection analysis on the traffic planning spatial analysis data after buffer analysis and the land use planning spatial data to obtain conformance analysis data; Data Governance Module: Conduct result data governance on the conformance analysis data, screen out data outliers caused by data vectorization and spatial buffer analysis, and eliminate element data with values less than the data outlier threshold; Use the screened conformance analysis data to perform data fusion according to the traffic planning indicator fields and land use planning indicator fields to obtain the fused conformance analysis data. Generate conformance analysis data statistical charts according to the statistical dimensions of the planning conformance data, and generate a conformance analysis data statistical report according to the data statistical report template; Multi-level Associated Conformance Analysis Display Module: Configure the data visualization display model for the conformance analysis data. Through the configuration of data association links, display the traffic and land use spatial conformance analysis data results, including the preprocessed traffic and land use spatial data, the spatially conformant analysis data after data governance, the conformance analysis statistical charts and reports, and establish a multi-level associated conformance analysis display model; Display Module: Used to visually demonstrate the output multi-level associated conformance analysis display model; The specific operation steps of the conformance analysis module are as follows; According to the data types of spatial data points, lines, and polygons, combined with two planning indicators of the traffic planning type and planning technical level, establish a mapping relationship model between planning indicators and buffer distances; Perform spatial buffer analysis on point and line element data according to the mapping relationship model between planning indicators and buffer distances. Buffer line data elements into rectangular polygon elements and point data elements into circular polygon elements, and add a buffer distance field; Perform spatial element intersection analysis on the traffic planning spatial data after buffer analysis and the land use planning spatial data, calculate the intersection of the geometric shapes of the two types of planning elements, and retain the intersecting part as the geometric shape and attribute table of the output data to obtain conformance analysis data; The specific operation steps of the data governance module are as follows: Use GIS tools to calculate the areas of the conformance analysis polygon elements and the lengths of the line elements, and add corresponding fields to the data attribute table; Through human-computer interaction for error correction, the user views and measures the conformance analysis data. If data elements with obvious intersection outliers are found, take the maximum measured value as the outlier threshold; Use the outlier threshold to screen the spatial data and remove conformance analysis data elements with length and area fields less than the outlier threshold. Secondary human-computer interaction for error correction can be performed; Eliminate the screened data outlier elements to obtain the conformance analysis data after data governance.

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