A Spatial Data Analysis and Processing Method and System

Abstract

The present invention discloses a spatial data analysis and processing method and system. The method comprises: obtaining analysis requirements for spatial data, the analysis requirements including: an analysis area, analysis objects, and attribute fields; obtaining multiple data tables from a business database related to both the analysis area and the analysis objects, performing coordinate conversion on each of the multiple data tables, and creating a spatial coordinate system corresponding to each data table; obtaining attribute information corresponding to each analysis object and attribute field within the analysis area based on the spatial coordinate system corresponding to each data table, and displaying the attribute information of the analysis objects in the analysis area by category according to the attribute fields. By extracting and classifying the attribute information of the objects within the analysis area, the readability and visualization of the data are enhanced, allowing users to intuitively understand and analyze the attribute distribution and characteristics of the spatial data.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a spatial data analysis and processing method and system. Background Art

[0002] In recent years, with the growing demand for economic development and land and space development, services and applications in land and space planning, environmental assessment, and refined urban management have become increasingly widespread. Land and space statistical analysis is an important part of these applications. Spatial object analysis in land and space statistical analysis primarily involves performing attribute statistics and topological calculations of spatial position relationships within a specific buffer range for specific business content (such as environmental protection objects, building types, industrial types, and residential types). Combined with temporal attributes, this constitutes the statistical analysis requirements for specific objects within a specific range, providing business support for scientific decision-making and planning management. Currently, many regions in China have established basic land planning databases and business application databases, including basic data and related business data.

[0003] At present, GIS (Geographic Information System) platform software has been widely used in many provinces, but in actual use, it is found that many business applications have the need to complete the attribute statistics of the business objects of concern and the topological analysis of spatial location relationships within a specific buffer range. Among them, completing the object analysis and statistics within a specific buffer range requires not only analyzing the objects and their attributes, but also paying attention to the spatial range to which they belong, that is, setting a certain spatial buffer range for the business objects; spatial topological relationships are the basis of all spatial analysis. Many application scenario analyses require the implementation of spatial topological analysis between different elements, and statistical analysis of the number of specified attribute elements contained in the spatial range of business elements. The spatial range can be expressed as points, lines, surfaces, bodies and other shapes. However, in traditional business applications, topological relationship calculations are time-consuming, affecting user experience and computing efficiency. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a spatial data analysis and processing method and system. By performing coordinate conversion on data tables in a data source and creating a spatial coordinate system, spatial standardization of data is achieved, thereby resolving the problem of inconsistent coordinate systems that may exist between different data sources. By extracting and classifying attribute information of objects within the analysis area, the readability and visualization of the data are enhanced, enabling users to intuitively understand and analyze the attribute distribution and characteristics of spatial data.

[0005] To solve the above technical problems, a first aspect of an embodiment of the present invention provides a spatial data analysis and processing method, comprising the following steps:

[0006] Acquiring analysis requirements for spatial data, the analysis requirements including: analysis area, analysis object, and attribute fields;

[0007] Acquire a plurality of data tables in a business database related to the analysis area and the analysis object, perform coordinate transformation on the plurality of data tables respectively, and create a spatial coordinate system corresponding to each of the data tables;

[0008] Based on the spatial coordinate system corresponding to each of the data tables, attribute information corresponding to each of the analysis objects and the attribute fields in the analysis area is obtained, and the attribute information of the analysis objects is classified and displayed according to the attribute fields in the analysis area.

[0009] Furthermore, after obtaining the attribute information corresponding to the attribute field of each analysis object in the analysis area, the method further includes:

[0010] Converting the plurality of data tables into the same spatial coordinate system;

[0011] Based on preset topological rules, a spatial topological rule check is performed on the data tables located in the same spatial coordinate system to identify and repair topological errors;

[0012] Perform spatial topological analysis on the repaired data tables to establish the association between the analysis objects.

[0013] Furthermore, converting the plurality of data tables into the same spatial coordinate system includes:

[0014] Selecting a spatial coordinate system of the data table as a reference coordinate system;

[0015] Obtaining coordinate system information of the spatial coordinate system of each of the other data tables, and obtaining projection difference and reference plane difference between the spatial coordinate system and the reference coordinate system;

[0016] Based on the projection difference and the reference plane difference, the spatial data in each of the other data tables is converted into a value corresponding to the reference coordinate system.

[0017] Furthermore, after obtaining the attribute information corresponding to the attribute field of each analysis object in the analysis area, the method further includes:

[0018] Get the same attribute fields or similar attribute fields in different data tables;

[0019] Establish attribute associations for the same or similar attribute fields in different data tables and display them according to the same category.

[0020] Furthermore, after obtaining the same attribute fields or similar attribute fields in different data tables, the method further includes:

[0021] Based on the field name, data type and data consistency of the similar attribute fields, it is determined whether the attribute fields in different data tables are the same attribute field.

[0022] Furthermore, the calculation formula for the similarity estimation value of similar attribute fields in different data tables is:

[0023] FSS=λ1×S name +λ2×S stat +λ3×S dist +λ4×S content +λ5×S semantic ;

[0024] Among them, FSS is the similarity estimation value of the attribute field, S name is the attribute field name similarity evaluation value, S stat is the statistical characteristic similarity estimate of the attribute field, S dist is the data distribution similarity estimate of the attribute field, S content is the data type similarity estimate of the attribute field, S semantic is the semantic similarity estimation value of the attribute field, and λ1, λ2, λ3, λ4, and λ5 are the weight coefficients of the corresponding estimation values.

[0025] Furthermore, the statistical characteristic similarity estimate S stat The calculation formula is:

[0026]

[0027]

[0028]

[0029] Among them, θ1 is the Euclidean distance weight coefficient value, θ2 is the cosine similarity weight coefficient value, D euclidean is the Euclidean distance similarity estimate, D max is the maximum possible value of Euclidean distance similarity, S cos is the estimated value of cosine similarity, μ1 and μ2 are the means of the two similar attribute field data, M1 and M2 are the medians of the two similar attribute field data, σ1 and σ2 are the variances of the two similar attribute field data, S1 and S2 are the skewness of the two similar attribute field data, and K1 and K2 are the kurtosis of the two similar attribute field data.

[0030] Furthermore, the analysis area includes: several fixed administrative areas or several designated areas.

[0031] Accordingly, a second aspect of an embodiment of the present invention provides a spatial data analysis and processing system, including:

[0032] A demand acquisition module is used to acquire analysis requirements of spatial data, wherein the analysis requirements include: analysis area, analysis object and attribute fields;

[0033] a data acquisition module, configured to acquire a plurality of data tables in a business database related to the analysis area and the analysis object, perform coordinate conversion on each of the plurality of data tables, and create a spatial coordinate system corresponding to each of the data tables;

[0034] A classification display module is used to obtain attribute information corresponding to each analysis object and the attribute field in the analysis area based on the spatial coordinate system corresponding to each data table, and classify and display the attribute information of the analysis object according to the attribute field in the analysis area.

[0035] Correspondingly, the third aspect of an embodiment of the present invention also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor performs the above-mentioned spatial data analysis and processing method.

[0036] In addition, a fourth aspect of an embodiment of the present invention further provides a computer-readable storage medium on which computer instructions are stored. When the instructions are executed by a processor, the above-mentioned spatial data analysis and processing method is implemented.

[0037] The above technical solutions of the embodiments of the present invention have the following beneficial technical effects:

[0038] 1. By performing coordinate conversion on the data tables in the data source and creating a spatial coordinate system, the spatial standardization of the data is achieved, thus solving the problem of inconsistent coordinate systems between different data sources;

[0039] 2. By extracting and classifying the attribute information of objects in the analysis area, the readability and visualization of the data are enhanced, allowing users to intuitively understand and analyze the attribute distribution and characteristics of spatial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of a spatial data analysis and processing method provided by an embodiment of the present invention;

[0041] Figure 2 This is a module block diagram of a spatial data analysis and processing system provided by an embodiment of the present invention.

[0042] Reference numerals:

[0043] 1. Demand acquisition module, 2. Data acquisition module, 3. Classification display module. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0045] Please refer to Figure 1 A first aspect of an embodiment of the present invention provides a spatial data analysis and processing method, comprising the following steps:

[0046] Step S100: Acquire analysis requirements of spatial data, where the analysis requirements include: analysis area, analysis object, and attribute fields.

[0047] Step S200 : obtaining a plurality of data tables in a business database related to both the analysis area and the analysis object, performing coordinate transformation on each of the plurality of data tables, and creating a spatial coordinate system corresponding to each data table.

[0048] Step S300 : Based on the spatial coordinate system corresponding to each data table, the attribute information corresponding to each analysis object and the attribute field in the analysis area is obtained, and the attribute information of the analysis object is classified and displayed according to the attribute field in the analysis area.

[0049] The present invention achieves spatial standardization of data by performing coordinate conversion on data tables in data sources and creating a spatial coordinate system, thereby solving the problem of inconsistent coordinate systems that may exist between different data sources. It also enhances the readability and visualization of data by extracting and classifying attribute information of objects in the analysis area, allowing users to intuitively understand and analyze the attribute distribution and characteristics of spatial data.

[0050] In one implementation of the present invention, after obtaining the attribute information corresponding to each analysis object and attribute field in the analysis area in step S300, the method further includes:

[0051] Step S311: converting multiple data tables into the same spatial coordinate system.

[0052] Step S312: Based on preset topology rules, a spatial topology rule check is performed on the data tables in the same spatial coordinate system to identify and repair topology errors.

[0053] Step S313: perform spatial topology analysis on the repaired data tables to establish the association between the analysis objects.

[0054] Through the above data processing process, multiple data tables are converted to the same spatial coordinate system, ensuring the consistency of the data in spatial position and eliminating errors caused by coordinate system differences; then, topology rule checking is performed based on preset topology rules, which can systematically identify topological errors in the data and improve data quality by repairing these errors; after repairing the topological errors, the spatial topological analysis can accurately establish the spatial relationship between the analysis objects, such as adjacency, intersection or inclusion, and more advanced spatial analysis can be performed, such as network analysis, overlay analysis and spatial statistical analysis.

[0055] Furthermore, the step S311 of converting the plurality of data tables into the same spatial coordinate system includes:

[0056] Step S311a: Select a spatial coordinate system of a data table as a reference coordinate system.

[0057] Step S312b: Obtain the coordinate system information of the spatial coordinate system of each other data table, and obtain the projection difference and reference plane difference between the spatial coordinate system and the reference coordinate system.

[0058] Step S313c: based on the projection difference and the reference plane difference, the spatial data in each other data table is converted into a value corresponding to the reference coordinate system.

[0059] By selecting the spatial coordinate system of one data table as the base coordinate system, a unified reference framework is provided for all data, simplifying spatial analysis and comparison across data tables. The coordinate system information of other data tables is then obtained and the projection and datum differences between them and the base coordinate system are calculated, ensuring the accuracy of the conversion process. By clearly distinguishing and processing projection and datum differences, conversion errors between different coordinate systems are effectively eliminated, improving the accuracy of spatial data. This process converts all data tables to the same base coordinate system, enhancing consistency between datasets and facilitating integrated analysis.

[0060] In another embodiment of the present invention, after obtaining the attribute information corresponding to each analysis object and attribute field in the analysis area in step S300, the method further includes:

[0061] Step S321: Acquire the same attribute fields or similar attribute fields in different data tables.

[0062] Step S322: Establish attribute associations for the same attribute fields or similar attribute fields in different data tables, and display them in the same category.

[0063] By identifying and associating the same or similar attribute fields in different data tables, it provides significant improvements in data integration, analytical depth and visualization effects, while simplifying data operation processes and enhancing data consistency; it also optimizes the execution of complex queries, promotes data reuse and sharing, and strengthens data governance to ensure data quality and accuracy.

[0064] Furthermore, after obtaining the same attribute fields or similar attribute fields in different data tables in step S322, the method further includes:

[0065] Step S323 : Based on the field names, data types and data consistency of similar attribute fields, it is determined whether the attribute fields in different data tables are the same attribute field.

[0066] Furthermore, in the above-mentioned process of determining the similarity of attribute fields, the similarity estimation value of two relatively similar attribute fields is first calculated, and then the calculation result is compared with the similarity threshold to determine whether the two similar attribute fields are the same attribute field.

[0067] When determining the threshold for comparing similarity estimates between similar attribute fields in different data tables, the primary considerations are typically business needs analysis and data characteristics assessment. Business needs analysis ensures that the threshold setting meets specific business objectives and decision-making criteria, while data characteristics assessment ensures the threshold is scientific and reasonable, taking into account data quality and consistency. Combining these two factors allows for the development of a threshold that reflects both business needs and data characteristics, thereby ensuring the accuracy and practicality of similarity comparisons.

[0068] The calculation formula for the similarity estimate of two similar attribute fields in different data tables is:

[0069] FSS=λ1×S name +λ2×S stat +λ3×S dist +λ4×S content +λ5×S semantic ;

[0070] Among them, FSS is the similarity estimation value of the attribute field, S name is the attribute field name similarity evaluation value, S stat is the statistical characteristic similarity estimate of the attribute field, S dist is the data distribution similarity estimate of the attribute field, S content is the data type similarity estimate of the attribute field, S semantic is the semantic similarity estimation value of the attribute field, and λ1, λ2, λ3, λ4, and λ5 are the weight coefficients of the corresponding estimation values.

[0071] The aforementioned attribute field name similarity evaluation values can use edit distance (such as Levenshtein distance) or Jaccard similarity coefficient to evaluate the similarity of field names; statistical characteristic similarity can be evaluated by calculating statistics such as the mean, median, variance, skewness, and kurtosis of two fields and using Euclidean distance and / or cosine similarity to evaluate the differences between them. Data distribution similarity can use the Kolmogorov-Smirnov test to evaluate the similarity of the data distributions of two fields. Data type similarity estimates can be used for numerical data and categorical data. For numerical data, the Pearson correlation coefficient or Spearman rank correlation is used to evaluate linear or nonlinear relationships; for categorical data, the Jaccard similarity or Kappa statistic is used. In addition, for semantic similarity estimates, natural language processing technologies such as word embedding or the BERT model can be used to evaluate the semantic similarity of field descriptions.

[0072] Furthermore, the statistical characteristic similarity estimate S stat The calculation formula is:

[0073]

[0074]

[0075]

[0076] Among them, θ1 is the Euclidean distance weight coefficient value, θ2 is the cosine similarity weight coefficient value, D euclidean is the Euclidean distance similarity estimate, D max is the maximum possible value of Euclidean distance similarity, S cos is the estimated value of cosine similarity, μ1 and μ2 are the means of the two similar attribute field data, M1 and M2 are the medians of the two similar attribute field data, σ1 and σ2 are the variances of the two similar attribute field data, S1 and S2 are the skewness of the two similar attribute field data, and K1 and K2 are the kurtosis of the two similar attribute field data.

[0077] Furthermore, the analysis area includes: several fixed administrative areas or several designated areas.

[0078] The following describes the spatial data analysis and processing process using a certain regional business requirement as an example:

[0079] First, all business data is brought into the regional big data center's data lake. The platform can connect to business databases on demand, achieving broad coverage of spatial computability. Second, after connecting to the business database, it can filter business tables and attribute fields within them, adding them to the platform for spatial analysis as needed. Business data is required to have spatial coordinates, achieving flexibility in spatial computability, and customizing static pages for adding data and selecting attribute fields. After selecting business tables and fields, they can be added to the analysis object layer. Third, one can freely configure and select one or more analysis objects for statistical analysis, including residential areas, buildings, stratified households, people, and / or commercial buildings as layer objects.

[0080] Furthermore, circles are divided into two parts: fixed circles (as shown in Table 1) and custom circles (as shown in Table 2). Among them, fixed circles can be selected by users in Table 1; custom circles can be drawn by users according to their interests. For example, the Hunan Road street will focus on and draw the area of ​​the internet celebrity block.

[0081] Table 1

[0082]

[0083]

[0084] Table 2

[0085] menu Remark Hunan Road Street Internet Celebrity Block Separate layer, you can draw your own schematic Custom circles Customize the drawing surface function, click to trigger the surface drawing

[0086] The output fields that can be displayed can be output in a variety of formats. In addition to the existing Excel format, it also supports specific formats such as reports, plots, and results sharing.

[0087] (1) Taking the housing as an example, the report content includes basic information of the housing, such as the community, building, number of rooms, total number of people, basic information of the people (household registration, age, gender, etc.), and labels selected by the user (such as elderly people living alone).

[0088] (2) Taking commercial buildings as an example, the report content includes basic information of the building, such as building name, number of buildings, number of resident companies, company type, industry category, number of employees, etc., as well as user-defined configuration selected labels (taxes, number of local companies, sales rate, vacancy rate, investment visits and viewings, etc.).

[0089] On this basis, statistics and analysis results are presented in the form of an interface, recording the user's analysis object layer and spatial range as input parameters, and the statistics and analysis results as output, which can be shared by the business end by calling the interface. Initially, this will be implemented statically on the page.

[0090] The above content is only used to describe the scenarios and processes to which the above spatial data analysis and processing method can be applied, but the protection scope of the technical solution of the present invention is not limited thereto.

[0091] Accordingly, please refer to Figure 2 A second aspect of an embodiment of the present invention provides a spatial data analysis and processing system, including:

[0092] Demand acquisition module 1 is used to obtain analysis requirements of spatial data, which include analysis area, analysis object and attribute fields;

[0093] Data acquisition module 2, which is used to obtain several data tables in the business database related to the analysis area and the analysis object, perform coordinate conversion on the several data tables respectively, and create a spatial coordinate system corresponding to each data table;

[0094] The classification display module 3 is used to obtain the attribute information corresponding to each analysis object and the attribute field in the analysis area based on the spatial coordinate system corresponding to each data table, and classify and display the attribute information of the analysis object according to the attribute field in the analysis area.

[0095] The functional modules in the above spatial data analysis and processing system correspond to the steps in the spatial data analysis and processing method. Each functional module can be further clarified into various functional units according to the detailed steps of the analysis and processing method to be consistent with the method, and will not be repeated here.

[0096] Correspondingly, the third aspect of an embodiment of the present invention also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor performs the above-mentioned spatial data analysis and processing method.

[0097] In addition, a fourth aspect of an embodiment of the present invention further provides a computer-readable storage medium on which computer instructions are stored. When the instructions are executed by a processor, the above-mentioned spatial data analysis and processing method is implemented.

[0098] The embodiment of the present invention is intended to protect a spatial data analysis and processing method and system, wherein the method includes the following steps: obtaining analysis requirements for spatial data, the analysis requirements including: analysis area, analysis object, and attribute fields; obtaining several data tables in a business database related to both the analysis area and the analysis object, performing coordinate conversion on each of the several data tables, and creating a spatial coordinate system corresponding to each data table; based on the spatial coordinate system corresponding to each data table, obtaining attribute information corresponding to each analysis object and attribute field in the analysis area, and displaying the attribute information of the analysis object in the analysis area by category according to the attribute field. The above technical solution has the following effects:

[0099] 1. By performing coordinate conversion on the data tables in the data source and creating a spatial coordinate system, the spatial standardization of the data is achieved, thus solving the problem of inconsistent coordinate systems between different data sources;

[0100] 2. By extracting and classifying the attribute information of objects in the analysis area, the readability and visualization of the data are enhanced, allowing users to intuitively understand and analyze the attribute distribution and characteristics of spatial data.

[0101] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A spatial data analysis and processing method, characterized in that: The steps include: Acquiring analysis requirements for spatial data, the analysis requirements including: analysis area, analysis object, and attribute fields; Acquire a plurality of data tables in a business database related to the analysis area and the analysis object, perform coordinate transformation on the plurality of data tables respectively, and create a spatial coordinate system corresponding to each of the data tables; Based on the spatial coordinate system corresponding to each of the data tables, acquiring attribute information corresponding to each of the analysis objects and the attribute fields in the analysis area, and displaying the attribute information of the analysis objects in the analysis area by category according to the attribute fields; After acquiring the attribute information corresponding to the attribute field of each analysis object in the analysis area, the method further includes: Get the same attribute fields or similar attribute fields in different data tables; Establish attribute associations for the same or similar attribute fields in different data tables and display them in the same category; After obtaining the same attribute fields or similar attribute fields in different data tables, the method further includes: Based on the field name, data type and data consistency of the similar attribute fields, determining whether the attribute fields in different data tables are the same attribute field; The calculation formula for the similarity estimation value of similar attribute fields in different data tables is: FSS=λ1×S name +λ2×S stat +λ3×S dist +λ4×S content +λ5×S semantic ; Among them, FSS is the similarity estimation value of the attribute field, S name is the attribute field name similarity evaluation value, S stat is the statistical characteristic similarity estimate of the attribute field, S dist is the data distribution similarity estimate of the attribute field, S content is the data type similarity estimate of the attribute field, S semantic is the semantic similarity estimation value of the attribute field, λ1, λ2, λ3, λ4, and λ5 are the weight coefficients of the corresponding estimation values; The statistical characteristic similarity estimate S stat The calculation formula is: Among them, θ1 is the Euclidean distance weight coefficient value, θ2 is the cosine similarity weight coefficient value, D euclidean is the Euclidean distance similarity estimate, D max is the maximum possible value of Euclidean distance similarity, S cos is the estimated value of cosine similarity, μ1 and μ2 are the means of the two similar attribute field data, M1 and M2 are the medians of the two similar attribute field data, σ1 and σ2 are the variances of the two similar attribute field data, S1 and S2 are the skewness of the two similar attribute field data, K1 and K2 are the kurtosis of the two similar attribute field data; After acquiring the attribute information corresponding to the attribute field of each analysis object in the analysis area, the method further includes: Converting the plurality of data tables into the same spatial coordinate system; Based on preset topological rules, a spatial topological rule check is performed on the data tables located in the same spatial coordinate system to identify and repair topological errors; Perform spatial topological analysis on the repaired data tables to establish the association between the analysis objects.

2. The spatial data analysis and processing method according to claim 1, characterized in that: The converting the plurality of data tables into the same spatial coordinate system includes: Selecting a spatial coordinate system of the data table as a reference coordinate system; Obtaining coordinate system information of the spatial coordinate system of each of the other data tables, and obtaining projection difference and reference plane difference between the spatial coordinate system and the reference coordinate system; Based on the projection difference and the reference plane difference, the spatial data in each of the other data tables is converted into a value corresponding to the reference coordinate system.

3. The spatial data analysis and processing method according to claim 1 or 2, characterized in that: The analysis area includes: several fixed administrative areas or several designated areas.

4. A spatial data analysis and processing system, characterized in that: Analyzing spatial data based on the spatial data analysis and processing method according to any one of claims 1 to 3 includes: A demand acquisition module is used to acquire analysis requirements of spatial data, wherein the analysis requirements include: analysis area, analysis object and attribute fields; a data acquisition module, configured to acquire a plurality of data tables in a business database related to the analysis area and the analysis object, perform coordinate conversion on each of the plurality of data tables, and create a spatial coordinate system corresponding to each of the data tables; A classification display module is used to obtain attribute information corresponding to each analysis object and the attribute field in the analysis area based on the spatial coordinate system corresponding to each data table, and classify and display the attribute information of the analysis object according to the attribute field in the analysis area.

5. An electronic device, characterized in that: include: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the spatial data analysis and processing method according to any one of claims 1 to 3.

Images