CN114331790B - Grid processing method and system for incomplete edges of population data - Google Patents
Grid processing method and system for incomplete edges of population data Download PDFInfo
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Abstract
The invention provides a method and a system for processing a population data edge incomplete grid, wherein the method for processing the population data edge incomplete grid comprises the following steps: acquiring population space data of a research area, and performing vectorization operation to generate population grid data; carrying out integrity judgment on the population grid data by adopting an area discrimination method, and extracting an incomplete edge grid; carrying out even number splitting on the grid with incomplete edges to obtain a split grid; judging split grids by adopting an area discrimination method, repeating the judgment of even number split and the area discrimination method when incomplete grids exist in the split grids and the area loss of the incomplete grids exceeds 50%, and obtaining fine grids when only complete grids exist in the split grids or the area loss of the incomplete grids does not exceed 50%; carrying out population division on the fine grid by adopting a spatial interpolation method; population grid data is generated. The invention combines multi-level spatialization and spatial interpolation, and improves the matching degree of the edge grid and the boundary of the research area.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a grid processing method and system for incomplete population data edges.
Background
With the rapid development of big data technology, data highly related to population is also endless, and the expressive force of population spatialization is more refined. The research of population data spatialization is to perform spatial interpolation through demographic and administrative division data, and develop to use multiple reference data and multi-element models which have high spatial correlation with population space distribution, such as POI (Point of interest), high-resolution images, topographic and topographic data, traffic road data, ground surface coverage data, noctilucent remote sensing data and the like, and the population spatialization models are increasingly abundant, so that the accuracy and the expression details of the result of the population spatialization data are greatly improved.
However, the accuracy of the data of the population mesh is limited due to the incomplete edges caused by the overlarge edge scale of the data of the population mesh or the non-fit of the data of the population mesh with the boundary of the study area.
Disclosure of Invention
In order to solve the above problems in the prior art, an object of the present invention is to provide a method and a system for processing domain correction of an incomplete mesh of human mouth data edges based on multi-level spatialization and spatial interpolation of human mouth mesh data.
In order to achieve the purpose, the invention adopts the following technical scheme:
according to a first aspect of the present invention, there is provided a method for processing a mesh with incomplete population data edges, comprising:
s1, acquiring population space data of a research area, and performing vectorization operation on the population space data to generate population grid data;
s2, based on the population grid data, adopting an area discrimination method to carry out integrity judgment, and extracting an incomplete edge grid;
s3, carrying out even number splitting on the incomplete edge grid to obtain a split grid;
s4, judging the split grids by adopting an area discrimination method, repeating the steps S3 and S4 when incomplete grids exist in the split grids and the area loss of the incomplete grids exceeds 50%, obtaining fine grids when only complete grids exist in the split grids or the area loss of the incomplete grids does not exceed 50%, and performing the step S5;
s5, based on the fine grids, carrying out population division by adopting a space interpolation method;
and S6, generating population grid data of the suitable research area.
Further, the population mesh data is population mesh data that has undergone incomplete edge mesh processing during the generation process or population mesh data that has not undergone incomplete edge mesh processing during the generation process.
Still further, the population mesh data that has been processed with the incomplete edge mesh in the generation process may be obtained by the following processing method:
s11, vectorizing the population space data to generate primary population grid data, performing area screening on the primary population grid data, and extracting incomplete edge grids of the primary population grid;
s12, storing the incomplete mesh of the edge of the primary population mesh as an independent image spot;
s13, taking the grid dimension of the primary population grid as an initial value, taking the determined grid interval as a grid refinement step length, and performing grid refinement operation on the independent image spots to generate refined independent image spots;
s14, area screening is carried out on the refined independent image spots, when the refined independent image spots have the phenomenon that complete grids and incomplete grids exist at the same time, grid intervals with smaller scales are selected to carry out population spatialization on the incomplete grids in the refined independent image spots, the steps S11, S12, S13 and S14 are repeatedly carried out, area screening, edge incomplete grids are extracted, refining operation is carried out, area screening is carried out again, and refined independent image spots with smaller grid scales are generated; when only complete grids or incomplete grids exist in the refined independent image spots, the grid refinement processing of the current refined independent image spots is finished, and grid refinement operation is stopped;
and S15, overlapping and merging the independent image spots subjected to the mesh refinement processing and the primary population mesh into a unified layer, and generating population mesh data subjected to the edge incomplete mesh processing.
Further, the spatial interpolation method is an area weight method or an inverse distance interpolation method.
Further, comparing the population data of the incomplete edge grid with that of the complete adjacent grid, and dividing the population of the fine grid by adopting an area weighting method under the condition that the population data of the incomplete edge grid is larger than that of the complete adjacent grid; and adopting an inverse distance interpolation method to divide the population of the fine grid under the condition that the population data in the edge incomplete grid is smaller than the population data in the adjacent complete grid.
According to a second aspect of the present invention, there is also provided a system for processing a mesh with incomplete population data edges, comprising:
the population grid data generation module is used for acquiring population space data of a research area and carrying out vectorization operation on the population space data to generate population grid data;
the incomplete edge grid extraction module is used for carrying out integrity judgment by adopting an area discrimination method based on the population grid data and extracting incomplete edge grids;
the splitting module is used for carrying out even number splitting on the incomplete edge grid to obtain a split grid;
the area judging module is used for judging the split grids by adopting an area judging method, repeatedly adopting the split module to carry out even number splitting and judging by adopting the area judging module under the condition that incomplete grids exist in the split grids and the area loss of the incomplete grids exceeds 50%, and obtaining fine grids under the condition that only complete grids exist in the split grids or the area loss of the incomplete grids does not exceed 50%;
the spatial interpolation module is used for dividing the population by adopting a spatial interpolation method based on the fine grid;
and the generating module is used for generating the population grid data suitable for the research area.
Further, the population mesh data is population mesh data that has undergone incomplete edge mesh processing during the generation process or population mesh data that has not undergone incomplete edge mesh processing during the generation process.
Still further, the population mesh data that has been processed with the incomplete edge mesh during the generation process may be acquired using a processing system comprising:
the extraction submodule is used for carrying out vectorization operation on the population space data to generate primary population grid data, carrying out area screening on the primary population grid data and extracting incomplete grids at the edges of the primary population grid;
the pattern spot storage submodule is used for storing the incomplete edge grid of the primary population grid as an independent pattern spot;
a refinement operation submodule, configured to perform a mesh refinement operation on the independent pattern spots by using the mesh scale of the primary population mesh as an initial value and using the determined mesh interval as a mesh refinement step length, so as to generate refined independent pattern spots;
the area screening submodule is used for carrying out area screening on the refined independent pattern spots, when the refined independent pattern spots have the phenomenon that complete grids and incomplete grids exist at the same time, selecting grid intervals with smaller scales to carry out population spatialization on the incomplete grids in the refined independent pattern spots, and repeatedly adopting the extraction submodule, the pattern spot storage submodule, the refining operation submodule and the area screening submodule to carry out processing so as to generate the refined independent pattern spots with smaller grid scales; when only a complete grid or an incomplete grid exists in the refined independent image spots, finishing the grid refinement treatment of the current refined independent image spots, and stopping grid refinement operation;
and the superposition merging submodule is used for superposing and merging the independent pattern spots subjected to the grid refinement processing and the primary population grid into a unified layer to generate population grid data.
Further, the spatial interpolation method is an area weight method or an inverse distance interpolation method.
Further, comparing the population data of the incomplete edge grid with that of the complete adjacent grid, and dividing the population of the fine grid by adopting an area weighting method under the condition that the population data of the incomplete edge grid is larger than that of the complete adjacent grid; and adopting an inverse distance interpolation method to divide the population of the fine grid under the condition that the population data in the edge incomplete grid is smaller than the population data in the adjacent complete grid.
The technical scheme provided by the invention has the beneficial effects that at least:
aiming at the problem that the accuracy of the data of the population grid is limited due to the fact that the edge grid is not attached to a vector boundary or the grid size greatly exceeds the boundary of a research area in the process of generating and using the existing population data spatialization, the invention provides a method and a system for processing the population grid with incomplete edges.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments will be briefly described below. Other features, objects, and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for processing a partial mesh of population data edges according to an embodiment of the present invention;
fig. 2 is a diagram illustrating a technical flow of a method for processing an edge incomplete mesh in a population data using process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an example of an edge-broken grid according to an embodiment of the present invention;
FIG. 4 is an illustration of an exemplary even-numbered split view of a partially complete grid according to an embodiment of the present invention;
fig. 5 is a diagram illustrating a technical flowchart of a method for processing an edge incomplete mesh in a population data generation process according to an embodiment of the present invention;
FIG. 6 is an exemplary illustration of an individual patch into which an incomplete mesh of edges of a primary population mesh may be stored, as provided by an embodiment of the present invention;
fig. 7(a) -7 (b) are comparative example diagrams before and after mesh refinement provided by the embodiment of the present invention, wherein fig. 7(a) is an example diagram of the boundary of a primary population mesh without mesh refinement provided by the embodiment of the present invention; FIG. 7(b) is a diagram illustrating an example of the boundaries of a population mesh that has been mesh refined according to an embodiment of the present invention;
fig. 8 is a diagram illustrating an exemplary structure of a grid processing system with incomplete population data edges according to an embodiment of the present invention.
Detailed Description
For a better understanding of the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that the detailed description is merely illustrative of exemplary embodiments of the present application and does not limit the scope of the present application in any way. Like reference numerals refer to like elements throughout the specification. The expression "and/or" includes any and all combinations of one or more of the associated listed items.
In the drawings, the size, dimension, and shape of elements have been slightly adjusted for convenience of explanation. The figures are purely diagrammatic and not drawn to scale. As used herein, the terms "approximately", "about" and the like are used as table-approximating terms and not as table-degree terms, and are intended to account for inherent deviations in measured or calculated values that would be recognized by one of ordinary skill in the art. In addition, in the present application, the order in which the processes of the respective steps are described does not necessarily indicate an order in which the processes occur in actual operation, unless explicitly defined otherwise or can be inferred from the context.
It will be further understood that terms such as "comprising," "including," "having," "including," and/or "containing," when used in this specification, are open-ended and not closed-ended, and specify the presence of stated features, elements, and/or components, but do not preclude the presence or addition of one or more other features, elements, components, and/or groups thereof. Furthermore, when a statement such as "at least one of" appears after a list of listed features, it modifies that entire list of features rather than just individual elements in the list. Furthermore, when describing embodiments of the present application, the use of "may" mean "one or more embodiments of the present application. Also, the term "exemplary" is intended to refer to an example or illustration.
Unless otherwise defined, all terms (including engineering and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In addition, the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1 and fig. 2, fig. 1 is a flowchart illustrating a method for processing a incomplete edge mesh in a population data using process according to an embodiment of the present invention, and fig. 2 is a flowchart illustrating a technical process of a method for processing a incomplete edge mesh in a population data using process according to an embodiment of the present invention. As shown in fig. 1 and fig. 2, the present invention provides a mesh processing method with incomplete population data edges, comprising: during the use of the demographic spatial data,
s1, acquiring population space data of the research area, and carrying out vectorization operation on the population space data to generate population grid data;
s2, as shown in fig. 3, based on the population grid data, performing overlay analysis on the population grid of the population grid data and the boundary of the research area, performing integrity judgment on the population grid by using an area discrimination method, and extracting an incomplete edge grid;
s3, as shown in fig. 4, performing even-numbered splitting on the incomplete edge mesh to obtain a split mesh;
s4, judging the split grids by adopting an area discrimination method, repeating the steps S3 and S4 when incomplete grids exist in the split grids and the area loss of the incomplete grids exceeds 50%, obtaining fine grids when only complete grids exist in the split grids or the area loss of the incomplete grids does not exceed 50%, and performing the step S5;
s5, based on the fine grid, dividing population by adopting a space interpolation method;
and S6, generating population grid data of the suitable research area.
Further, the method for acquiring the population space data of the research area comprises the steps of acquiring the population data in the research area statistical yearbook from a local statistical bureau of an administrative area to which the research area belongs, and distributing the population data to each building class according to the area of a residential area; acquiring population data of a pattern spot level from geographical national condition data; spatial interpolation is obtained by combining various data such as night light and the like. The invention does not limit the acquisition method of the population space data of the research area.
Further, when the population space data is not population grid data, performing vectorization operation on the population space data according to the resolution of the population space data to generate population grid data, namely, establishing a vector grid on the non-vector population space data to generate population grid data.
Further, the population mesh data is population mesh data that has undergone incomplete edge mesh processing during the generation process or population mesh data that has not undergone incomplete edge mesh processing during the generation process.
Further, referring to fig. 5, fig. 5 is a diagram illustrating a technical flow of a processing method for an incomplete edge mesh in a population data generating process according to an embodiment of the present invention, wherein in the population space data generating process, population mesh data that has been processed by the incomplete edge mesh may be obtained by the following processing method:
s11, vectorizing the human mouth space data to generate primary population grid data, carrying out area screening on the primary population grid data, and extracting incomplete edge grids of the primary population grid;
s12, storing the incomplete mesh of the edge of the primary population mesh as an independent patch as a whole as shown in fig. 6;
s13, taking the grid dimension of the primary population grid as an initial value, taking the determined grid interval as a grid refinement step length, and performing grid refinement operation on the independent image spots to generate refined independent image spots;
and S14, area screening is carried out on the refined independent image spots. When the phenomenon that a complete grid and an incomplete grid exist simultaneously in the refined independent image spot exists, selecting a grid interval with smaller scale to perform population spatialization on the incomplete grid in the refined independent image spot, repeatedly performing the steps S11, S12, S13 and S14, accumulating in the process, performing area screening, extracting the incomplete grid at the edge, performing refining operation, performing area screening again, and generating the refined independent image spot with smaller grid scale; when only complete grids or incomplete grids exist in the refined independent image spots, the grid refinement processing of the current refined independent image spots is finished, and grid refinement operation is stopped;
and S15, overlapping and merging all independent image spots which are subjected to the grid refinement processing and have different scales with the primary population grid into a unified image layer, and generating population grid data subjected to the edge incomplete grid processing.
Further, the area screening of the primary population grid data specifically comprises: determining the area of a complete grid in the primary population grid, setting the area to be a unified area value of the primary population grid, carrying out area screening on all grids in the primary population grid, and when the area value of the grids is smaller than the unified area value, the grids are incomplete grids at the edge of the primary population grid.
Further, the grid spacing should be determined based on the actual situation. The invention proposes that when the grid size of the initial population grid is 1000 meters or more, the grid interval should be 500 meters-1000 meters; when the grid size of the initial population grid is 500-1000 m, the grid interval is 50-100 m; when the grid dimension of the initial population grid is below 500 meters, the grid interval should be 10-50 meters.
Furthermore, the grid refinement operation is to perform population spatialization on the independent image patches in a smaller scale according to the selected grid intervals, and the population spatialization method includes, but is not limited to, a distance weighting method, various spatial interpolation methods, and the like.
Furthermore, when only the complete grid or the incomplete grid exists in the refined independent image spots, the refined independent image spots reach the maximum crushing degree, and the grid refinement processing is not required to be continuously carried out, so that the grid refinement processing of the refined independent image spots is finished, and the grid refinement operation is stopped.
Further, the step of superposing and merging all the independent patches subjected to mesh refinement processing with different scales and the primary population mesh into a unified layer includes: and (4) arranging and stacking all the finely processed independent image spots with different scales and the initial population grid from top to bottom in sequence according to the grid scale from large to small, and combining the images into a unified image layer. As shown in fig. 7(a) and fig. 7(b), fig. 7(a) and fig. 7(b) are exemplary diagrams illustrating a pre-refinement and a post-refinement of a mesh provided by an embodiment of the present invention, wherein fig. 7(a) is an exemplary diagram illustrating a boundary of a primary population mesh provided by an embodiment of the present invention without mesh refinement; FIG. 7(b) is a diagram illustrating an example of the boundaries of a population mesh that has been mesh refined according to an embodiment of the present invention; the boundary representation precision of the population grid subjected to grid refinement operation can be obviously improved, and the boundary of a research area is more fit.
Further, the integrity judgment by the area judgment method includes: determining the complete grid area of grids in the population grid data, setting the complete grid area as a uniform area value of the population grids, performing area screening on each grid in the population grid, and when the grid area is equal to the uniform area value of the population grids, the grids are complete, so that the population grid data of a research area corresponding to the grids are suitable for the research area; when the grid area is less than the uniform area value of the population grid, the grid is incomplete, and all the incomplete grids at the edges are screened out.
Further, the even splitting of the incomplete edge mesh comprises: and extracting the maximum range of each incomplete grid at the edge, and splitting the grid into 2 multiplied by 2 according to the maximum range in an even-numbered way.
Furthermore, the spatial interpolation method is an area weight method or an inverse distance interpolation method.
Further, comparing the population data of the incomplete edge grid with the population data of the adjacent complete grid, and adopting an area weighting method to divide the population of the fine grid, wherein the population data of the incomplete edge grid is larger than the population data of the adjacent complete grid; and adopting an inverse distance interpolation method to divide the population of the fine grid under the condition that the population data in the marginal incomplete grid is smaller than the population data in the adjacent complete grid. The reason is that if the population data in the edge incomplete grid is larger than the population data in the adjacent complete grid, the population distribution in the area is distributed and diffused to the periphery by taking the area of the edge incomplete grid as a central area, and in order to ensure the authenticity of the population data, an area weighting method taking the incomplete grid as the center is adopted; if the population data in the incomplete edge mesh is smaller than that in the adjacent complete mesh, it indicates that the central region of the population distribution is not the incomplete edge mesh region, so an inverse distance weighting method that does not center on the incomplete mesh is used. The boundary representation precision of the processed population grid is obviously improved, the population grid is more fit with the boundary of a research area, and the matching degree of the edge grid and the boundary of the research area is greatly improved.
Further, the fine mesh is demographically divided using an area weighting method to assign the population data as a proportion of the area of the fine mesh to the area of the incomplete edge mesh that is not split by an even number. Because population classification by the area weighting method can obviously reduce population data compared with population grid data of an incomplete grid at the edge without even number splitting, the high value distribution condition of the population cannot be accurately shown, and the represented grades of all fine grids are greatly reduced, the following formula calculation is carried out on the population data again after the area weighting method is completed for population classification:
wherein the content of the first and second substances,
for an incomplete grid population at the edge where even-numbered splitting is not performed,
the population of the largest fine grid among the fine grids,
the number of populations allocated to each fine grid,
the calculated population for each fine grid.
Furthermore, the population division of the fine grids by adopting the inverse distance interpolation method is to calculate the distance between the centroid position of the adjacent complete grids as a starting point and the centroid of each fine grid as a weight, and redistribute the population data in each fine grid by taking the population number of the adjacent complete grids as an initial value.
The grid processing method with incomplete population data edges provided by the invention at least has the following beneficial effects:
aiming at the problem that the accuracy of the data of the population grids is limited due to the fact that edge grids are not attached to vector boundaries or the grid scales greatly exceed boundaries of a research area in the existing population data spatialization generation and use processes, the invention provides a method and a system for processing the edge incomplete grids of the population data.
As shown in fig. 8, the present invention further provides a system for processing a partial mesh with demographic data edges, comprising:
the population grid data generation module is used for acquiring population space data of a research area and carrying out vectorization operation on the population space data to generate population grid data;
the incomplete edge grid extraction module is used for carrying out integrity judgment by adopting an area discrimination method based on population grid data to extract incomplete edge grids;
the splitting module is used for carrying out even-numbered splitting on the grid with incomplete edges to obtain split grids;
the area judging module is used for judging the split grid by adopting an area judging method, repeatedly adopting the splitting module to carry out even-numbered splitting and judging by adopting the area judging module under the condition that the split grid has incomplete grids and the area loss of the incomplete grids exceeds 50 percent, and obtaining a fine grid under the condition that the split grid only has complete grids or the area loss of the incomplete grids does not exceed 50 percent;
the spatial interpolation module is used for dividing population by adopting a spatial interpolation method based on the fine grid;
and the generating module is used for generating the population grid data suitable for the research area.
Further, the population mesh data is population mesh data that has undergone incomplete edge mesh processing during the generation process or population mesh data that has not undergone incomplete edge mesh processing during the generation process.
Still further, the population mesh data that has been processed with the incomplete edge mesh during the generation process may be acquired using a processing system as follows:
the extraction submodule is used for carrying out vectorization operation on the human mouth space data to generate primary population grid data, carrying out area screening on the primary population grid data and extracting incomplete grids at the edges of the primary population grid;
the pattern spot storage submodule is used for storing the incomplete grids at the edge of the primary population grid as independent pattern spots;
the refinement operation submodule is used for taking the grid dimension of the primary population grid as an initial value, taking the determined grid interval as a grid refinement step length, and performing grid refinement operation on the independent image spots to generate refined independent image spots;
the area screening submodule is used for carrying out area screening on the refined independent pattern spots, when the refined independent pattern spots have the phenomenon that complete grids and incomplete grids exist at the same time, selecting grid intervals with smaller scales to carry out population spatialization on the incomplete grids in the refined independent pattern spots, and repeatedly adopting the extraction submodule, the pattern spot storage submodule, the refining operation submodule and the area screening submodule to carry out processing so as to generate the refined independent pattern spots with smaller grid scales; when only complete grids or incomplete grids exist in the refined independent image spots, the grid refinement processing of the current refined independent image spots is finished, and grid refinement operation is stopped;
and the superposition merging submodule is used for superposing and merging the independent pattern spots subjected to the grid refinement processing and the primary population grid into a unified layer to generate population grid data.
Further, the spatial interpolation method is an area weight method or an inverse distance interpolation method.
Further, comparing the population data of the incomplete edge grid with the population data of the complete adjacent grid, and dividing the population of the fine grid by adopting an area weighting method under the condition that the population data of the incomplete edge grid is larger than the population data of the complete adjacent grid; the condition for dividing the population of the fine grid by adopting the inverse distance interpolation method is that the population data in the marginal incomplete grid is smaller than the population data in the adjacent complete grid.
The grid processing system with incomplete population data edges provided by the invention at least has the following beneficial effects:
aiming at the problem that the accuracy of the data of the population grid is limited due to the fact that the edge grid is not attached to a vector boundary or the grid size greatly exceeds the boundary of a research area in the process of generating and using the existing population data spatialization, the invention provides a method and a system for processing the population grid with incomplete edges.
The objects, technical solutions and advantageous effects of the present invention are further described in detail with reference to the above-described embodiments. It should be understood that the above description is only a specific embodiment of the present invention, and is not intended to limit the present invention. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims (6)
1. A method for mesh processing with incomplete edges of population data, comprising:
s1, acquiring population space data of a research area, and performing vectorization operation on the population space data to generate population grid data;
s2, based on the population grid data, adopting an area discrimination method to carry out integrity judgment, and extracting an incomplete edge grid;
s3, carrying out even number splitting on the incomplete edge grid to obtain a split grid;
s4, judging the split grids by adopting an area discrimination method, repeating the steps S3 and S4 when incomplete grids exist in the split grids and the area loss of the incomplete grids exceeds 50%, obtaining fine grids when only complete grids exist in the split grids or the area loss of the incomplete grids does not exceed 50%, and performing the step S5;
s5, based on the fine grids, carrying out population division by adopting a space interpolation method;
s6, generating population grid data suitable for the research area;
wherein, the space interpolation method is an area weight method or an inverse distance interpolation method;
comparing the population data of the incomplete edge grid with the population data of the complete adjacent grid, and dividing the population of the fine grid by adopting an area weighting method under the condition that the population data of the incomplete edge grid is larger than the population data of the complete adjacent grid; and adopting an inverse distance interpolation method to divide the population of the fine grid under the condition that the population data in the edge incomplete grid is smaller than the population data in the adjacent complete grid.
2. The method of claim 1, wherein the population mesh data is population mesh data that has undergone incomplete mesh processing during the generating process or population mesh data that has not undergone incomplete mesh processing during the generating process.
3. The method of partial edge mesh processing of population data as in claim 2, wherein the population mesh data that has been processed by the partial edge mesh in the generating step is obtained by:
s11, carrying out vectorization operation on the population space data to generate primary population grid data, carrying out area screening on the primary population grid data, and extracting incomplete edge grids of the primary population grid;
s12, storing the incomplete mesh of the edge of the primary population mesh as an independent image spot;
s13, taking the grid dimension of the primary population grid as an initial value, taking the determined grid interval as a grid refinement step length, and performing grid refinement operation on the independent image spots to generate refined independent image spots;
s14, area screening is carried out on the refined independent image spots, when the refined independent image spots have the phenomenon that complete grids and incomplete grids exist at the same time, grid intervals with smaller scales are selected to carry out population spatialization on the incomplete grids in the refined independent image spots, the steps S11, S12, S13 and S14 are repeatedly carried out, and the refined independent image spots with smaller grid scales are generated; when only a complete grid or an incomplete grid exists in the refined independent image spots, the grid refinement processing of the current refined independent image spots is finished, and grid refinement operation is stopped;
and S15, overlapping and merging the independent image spots subjected to the mesh refinement processing and the primary population mesh into a unified layer, and generating population mesh data subjected to the edge incomplete mesh processing.
4. A system for grid processing with incomplete edges of population data, comprising:
the population grid data generation module is used for acquiring population space data of a research area and carrying out vectorization operation on the population space data to generate population grid data;
the incomplete edge grid extracting module is used for carrying out integrity judgment by adopting an area discrimination method based on the population grid data and extracting incomplete edge grids;
the splitting module is used for carrying out even splitting on the incomplete edge grid to obtain a split grid;
the area judging module is used for judging the split grids by adopting an area judging method, repeatedly adopting the split module to carry out even number splitting and judging by adopting the area judging module under the condition that incomplete grids exist in the split grids and the area loss of the incomplete grids exceeds 50%, and obtaining fine grids under the condition that only complete grids exist in the split grids or the area loss of the incomplete grids does not exceed 50%;
the spatial interpolation module is used for dividing the population by adopting a spatial interpolation method based on the fine grid;
a generation module for generating population grid data suitable for a study area;
wherein the spatial interpolation method is an area weight method or an inverse distance interpolation method;
comparing the population data of the incomplete edge grid with the population data of the adjacent complete grid, and dividing the population of the fine grid by adopting an area weighting method under the condition that the population data of the incomplete edge grid is greater than the population data of the adjacent complete grid; and adopting an inverse distance interpolation method to divide the population of the fine grid under the condition that the population data in the edge incomplete grid is smaller than the population data in the adjacent complete grid.
5. The system of claim 4, wherein the human mesh data is human mesh data for which the edge partial mesh processing has been performed during the generation process or human mesh data for which the edge partial mesh processing has not been performed during the generation process.
6. The system for processing a population data edge incomplete mesh as in claim 5, wherein the population mesh data that has been edge incomplete mesh processed during the generating process is obtained using a processing system comprising:
the extraction submodule is used for carrying out vectorization operation on the population space data to generate primary population grid data, carrying out area screening on the primary population grid data and extracting incomplete grids at the edges of the primary population grid;
the pattern spot storage submodule is used for storing the incomplete grids at the edge of the primary population grid as independent pattern spots;
a refinement operation submodule, configured to perform a mesh refinement operation on the independent pattern spots by using the mesh scale of the primary population mesh as an initial value and using the determined mesh interval as a mesh refinement step length, so as to generate refined independent pattern spots;
the area screening submodule is used for carrying out area screening on the refined independent pattern spots, when the refined independent pattern spots have the phenomenon that complete grids and incomplete grids exist at the same time, selecting grid intervals with smaller scales to carry out population spatialization on the incomplete grids in the refined independent pattern spots, and repeatedly adopting the extraction submodule, the pattern spot storage submodule, the refining operation submodule and the area screening submodule to carry out processing so as to generate the refined independent pattern spots with smaller grid scales; when only complete grids or incomplete grids exist in the refined independent image spots, the grid refinement processing of the current refined independent image spots is finished, and grid refinement operation is stopped;
and the superposition merging submodule is used for superposing and merging the independent pattern spots which finish the grid refinement treatment and the primary population grid into a unified layer to generate population grid data.
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