CN114090937A - Automatic urban spatial feature area division system - Google Patents
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Abstract
The invention discloses an automatic dividing system for urban spatial characteristic areas, which comprises: the client is used for acquiring the form content filled by the user; the browser is used for splicing the form contents into a long character string and transmitting the long character string to the background server along with the fact that a user clicks a submission button; the network server is used for processing the request of the browser and returning a corresponding response; the background server analyzes the received character string and executes geographic calculation in the background; after the calculation is finished, transmitting the position and the file name of the generated ShapeFile compression file to a browser; the browser analyzes the received data, converts the ShapeFile compressed file into a GeoJson format, and superimposes the result calculated by the background server on the map base map on the right side of the browser page for display. The remarkable effects are as follows: various region division algorithms are designed, and results can be compared and the algorithm more suitable for actually dividing the regions is selected.
Description
Technical Field
The invention relates to the technical field of town geographic management, in particular to an automatic urban spatial feature area dividing system.
Background
Along with the increasing of urban population, the increasing of urban scale, more and more areas needing to be controlled by governments, the contradiction between urban planning and urban development is increasingly prominent, and how to improve the urban planning quality under a new development situation and relate planning data of each level is an important problem facing the well-done urban planning at present. However, at present, the use of various city planning data by city managers is insufficient, and manual processing is frequently performed, which results in low efficiency. Meanwhile, the urban geographic information system is greatly developed and widely applied in recent years, and the computer technology, especially the Web technology and the corresponding front-end and back-end interaction technology are rapidly developed, so that the urban GIS system gradually forms a front-end and back-end integrated framework. Once the service is released, the interaction with the urban GIS system can be realized only through a browser, and more convenience and rapidness are provided for managers and users in cities.
Many spatial interaction data sets are analyzed on the basis of predefined regional units, such as, for example, the university admission rates of various cities, the movement of immigration, and the like. Meanwhile, various techniques and models have been developed to discover patterns in spatially interactive data. For example, a spatial interaction network is used as a graph, wherein regions are converted into nodes, and interaction flows embedded in a space are represented by weighted edges. Some graph partitioning methods found in complex network and computer science literature have been applied to the study of community detection or pattern discovery.
In recent years, much research in city geographic management focuses on grid management, and city areas are divided into individual regular or irregular grids through a spatial database and are coded, so that the management efficiency of cities is greatly improved. Most of the work in the aspect of urban area division is based on traffic track data, and a generally applicable system is not formed. According to the research of related papers, the work of enclosing a feature region by using any interest point as a seed point through a certain search weight has not been researched yet. In the aspect of city management and planning, a manager of a city needs to be able to quickly enclose a city feature area meeting specific requirements according to various point-like, linear and planar planning data of the city, and to present the result in an interactive manner, so as to meet the personalized requirements of a city planner.
The main problems of the present urban zoning are as follows: (1) the partition idea is mainly static, and static information such as POI and the like is directly clustered to complete partition; (2) classifying static data such as POI and the like, and expressing the region by the classified POI; (3) and clustering the track data, and constructing a Voronoi diagram by using a clustering center to complete partitioning.
The above methods do not adequately consider human activity and the connections between regions. Therefore, the partitioning result under the guidance of the static partitioning idea has certain limitations. In addition, the grid management mentioned above is more focused on efficient management of cities, and the division of the urban functional areas based on traffic big data is more focused on the research of algorithms, so that a set of universal processing method and flow for the GIS system of each large city is not formed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an automatic urban spatial characteristic area dividing system, which is based on the consideration of the common influence of various partition limiting factors to solve the problems existing in urban partitions, and finally realizes the accurate division of urban areas.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the utility model provides an automatic division system of city spatial feature region which the key lies in: including user, browser, web server and backstage server, wherein:
the user side is used for acquiring the form content filled by the user;
the browser is used for splicing the form contents into a long character string and transmitting the long character string to the background server along with the fact that a user clicks a submission button;
the network server is used for processing the request of the browser and returning a corresponding response;
the background server analyzes the received character string, initializes a construction function of the built-in algorithm by using the analyzed parameter, and executes geographical calculation in the background; after the calculation is finished, transmitting the position and the file name of the generated ShapeFile compression file to a browser;
the browser analyzes the received data, converts the ShapeFile compressed file into a GeoJson format, and superimposes the result calculated by the background server on the map base map on the right side of the browser page for display.
Further, a WebSocket protocol is adopted for communication between the background server and the browser.
Further, the algorithm built in the background server comprises a vector expansion method, a vector frame determination method and a grid expansion method.
Further, the vector extrapolation method comprises the following calculation processing steps:
a1, preprocessing data:
generating a road surface map layer through the road line map layer;
generating coverage data by the road surface map layer to obtain a topological relation;
extracting a line vector and a plane vector with a topological relation from the generated coverage data;
spatial connection, and mapping to obtain a line vector with weight attributes;
a2, merging regions:
obtaining the topological relation between the edge and the polygon;
performing space query operation through the input point layer and the polygon layer to obtain a polygon where the point is as a starting point of search;
enabling the id in the attribute of the original polygon to be 0, and performing updating operation to enable the id of the polygon searched from different starting points to be the same;
iterative search is carried out;
outputting a search result and updating the face element id;
a3, post-treatment:
erasing the extracted area according to the input water system layer, and obtaining a final result layer after the layer in post-treatment is erased;
a4, output:
and outputting the compressed file of ShapeFile.
Further, the vector framing method comprises the following processing steps:
b1, extracting line segments with the same grade according to the grade of the line segments, and generating a bounding region by using a FeatureToPolygon tool;
b2, respectively carrying out space query on the tested points and the enclosure areas with different levels generated in the step B1 to obtain the boundaries of the test points in the enclosure areas with different levels;
and B3, combining the obtained boundaries and outputting a result.
Further, the grid expanding method comprises the following processing steps:
c1, extracting a certain grade line from the original road map layer to generate raster data as seed expansion;
c2, extracting coordinates of points of the input test point layer, calculating to obtain row and column numbers of the coordinates in the grid data, and taking the row and column numbers as seed growing points;
and C3, taking the 4-neighborhood as a search range, carrying out growth search by using the seed points in the step C2, outputting different raster files when the boundary or the road with the corresponding level is searched for at the end condition of the search, and converting the raster files into vector data.
Further, the vector expanding method, the vector frame determining method and the grid expanding method are input by a search weight, a point layer, a line layer, a surface layer and corresponding weights.
Further, when the search weight selects the level N, the system calculates the enclosing areas of the level N-1, the level N-2, the level 1 and identifies the enclosing areas by different colors, wherein N is an integer larger than 1.
Furthermore, an algorithm selection area, a function selection area, an operation selection area and a result display area are designed on the page of the browser, and the algorithm selection area is used for selecting different algorithms according to different input data formats to realize area division; the function selection area is used for realizing algorithm input selection, data input selection and weight input selection; the operation selection area is provided with a submission button, a storage button and a clearing button, and the submission button is clicked after the user finishes filling the form; after the browser loads the region division result, the generated ShapeFile compressed file can be saved to any position by clicking the saving button; clicking the clear button may remove the region expansion results loaded on the map on the right side from the map base.
The invention has the following remarkable effects:
1. various region division algorithms are designed, and different algorithms can be switched according to whether the data is vector or raster data; the system can support the input of various surface feature element types, and set different weight grades for each input element to carry out reasonable city area automatic division; different algorithm selections, input element numbers, weight grades and the like can be carried out according to the preference and the requirement of the user, and different region division results are obtained by selecting different input interest point elements; the results can be compared to select an algorithm that is more appropriate for the actual region division;
2. compared with the traditional urban area division mode, the system also has the following advantages:
the use is convenient: the user can compare and analyze the characteristic region division results of various algorithms only through the browser, and compared with the traditional desktop end for geographic information data processing, the method can display the result of the city after the region division is output to the user according to the setting rule selected by the user, and can support the user to select the result under different settings; by embedding the three algorithms into a front-end and back-end interactive framework, a user can check automatically divided feature areas on a map base map of a Web interface only by filling and submitting a form in a browser, so that the efficiency of searching the feature areas in a city is greatly improved;
the mobility is strong: the front-end and back-end interactive system shell can be embedded with other various types of geographic computing methods without making great changes to the existing system;
the expansibility is strong: the Python-based back-end server can realize more complex functions in a packet reference mode;
the algorithm has strong robustness: in the process of testing the data set, aiming at places with incomplete data cleaning, such as suspension points, broken lines and other data sets, the influence on the algorithm is small, the correct regular regional division of the urban regional division result can be realized on the obtained standard data set, and the fault tolerance rate of the algorithm is good;
sharing and visual analysis: based on a B/S thin client architecture, automatic area division of front-end and back-end interaction is realized, and sharing and publishing services and visual analysis of data are realized under strong support of the Internet.
Drawings
FIG. 1 is a system architecture diagram of the present invention;
FIG. 2 is an overall design of the present system;
FIG. 3 is an interface diagram of the present system;
FIG. 4 is a diagram illustrating the result of region partition by vector extrapolation;
FIG. 5 is a diagram illustrating the result of region partition by the vector framing method;
fig. 6 is a schematic diagram of the region division result of the grid expansion method.
Detailed Description
The following provides a more detailed description of the embodiments and the operation of the present invention with reference to the accompanying drawings.
As shown in fig. 1, an automatic urban spatial feature area partitioning system includes a user side, a browser, a web server, and a background server, where the background server and the browser communicate with each other by using a WebSocket protocol, where:
the user side is used for acquiring the form content filled by the user;
the browser is used for splicing the form contents into a long character string and transmitting the long character string to the background server along with the fact that a user clicks a submission button;
the network server is used for processing the request of the browser and returning a corresponding response;
the background server analyzes the received character string, initializes a construction function of the built-in algorithm by using the analyzed parameter, and executes geographical calculation in the background; after the calculation is finished, transmitting the position and the file name of the generated ShapeFile compression file to a browser;
the browser analyzes the received data, converts the ShapeFile compressed file into a GeoJson format, and superimposes the result calculated by the background server on the map base map on the right side of the browser page for display.
In this example, the front-end and back-end information interaction mechanism of the system is realized by relying on a WebSocket protocol. WebSocket is a protocol originally provided by HTML5 for full-duplex communication over a single TCP connection. WebSocket enables data exchange between a client and a server to be simpler, and allows the server to actively push data to the client. In the WebSocket API, the browser and the server only need to complete one handshake, and persistent connection can be directly established between the browser and the server, and bidirectional data transmission is carried out.
The overall design of the front end and the back end of the system is shown in the following figure 2, and comprises three parts, namely page design and beautification, front end development and background algorithm development, wherein the design process of the page design and the beautification comprises interface organization and design and CSS style design, and the designed page resources and CSS files are output to a front end development part; the design process of the front-end development part comprises front-end visual composition design, user interaction mode design, message transmission process setting, object-oriented message structure design, user interface input design and data analysis, analyzed data, page resources and CSS files are integrated together with components and then output to a background algorithm development part for feature partition visualization or other migration application, wherein the front-end visual composition design is also assisted to street organization and design, and the user interaction mode design is also used for CSS style design; the background algorithm development comprises urban area division data, characteristic division algorithm implementation, algorithm interface parameter setting, data service and transmission format setting and WebSock background data service construction, background data and a service configuration file formed after the WebSock background data service construction is completed are sent to perform data analysis, wherein the characteristic division algorithm implementation also depends on a user interaction mode design result, the algorithm interface parameter setting also depends on a message transmission flow setting result, and the data service and transmission format setting depends on an object-oriented message structure design result.
In the actual interaction process of the system, a 7000 port is adopted by a WebSocket protocol, a browser firstly acquires form contents filled by a user through jQuery, and then splices the form contents into a long character string which is transmitted to a background server along with the user clicking a submit button. The background server receives and analyzes the character string transmitted by the browser, and then initializes the construction functions of the three algorithms by using the analyzed parameters, thereby executing complex geographic calculation in the background. After the background calculation is completed, the background transmits the position and the file name of the generated ShapeFile compression packet to the front end, the front end analyzes the ShapeFile compression packet by adopting a similar method, the browser converts the generated ShapeFile file into a GeoJson format, and finally, the effect of superposing the result of the background calculation to the map base map on the right side of the Web page is realized by utilizing related tools in the leaflet.
Referring to fig. 3, the interface of the system on the browser is shown in fig. 3, the top of the interface is the name of the system, and the bottom of the name is three tabs, which respectively correspond to three algorithms for automatic division of the area. Each tab carries a form on the left and a map base on the right underneath it. The current algorithm name is displayed in the form on the left side, and the user is required to input the search weight and select a face layer, a line layer, a road layer and a starting point file. Wherein the user is required to specify the corresponding weight since the face map layer and the line map layer themselves have no weight field. There are three buttons below the form, a submit button, a save button, and a clear button. And when the user finishes filling the form and clicks the submit button, the front end transmits the form filled by the user to the background server through the WebSocket protocol. After the front end finishes loading the region division result, the generated ShapeFile compressed packet can be stored to any position by clicking the storage button. Clicking the clear button may remove the region expansion results loaded on the right map from the base map.
The map resource on the right side is from an OSM online map, and the lightweight JS library of the LEAFLET is adopted for operating the map object and setting the superposition and style of the vector layer.
In this embodiment, the algorithm built in the background server includes a vector expansion method, a vector frame determination method, and a grid expansion method. The three algorithms have the same input, are all search weight, point layer, line layer, surface layer and corresponding weight, and are different only in the difference of the algorithms, so that convenience is provided for comparison of the three algorithms. The input layer of the algorithm test comprises a path line layer (the weights are arranged from large to small: 1,2,3 and 4), a water system line layer, a water area layer and a test point set. Specifically, the method comprises the following steps:
(1) the vector external expansion method comprises the following calculation processing steps:
a1, preprocessing data:
generating a road surface map layer through the road line map layer;
generating coverage data by the road surface map layer to obtain a topological relation;
extracting a line vector and a plane vector with a topological relation from the generated coverage data;
spatial connection, and mapping to obtain a line vector with weight attributes;
a2, merging regions:
obtaining the topological relation between the edge and the polygon; there are two topological relations, one is the polygon at the left and right ends of the line, and the second is which edges the polygon contains. The second topological relationship may be inferred from the first topological relationship. For convenience of the above operation, the established topological relation also includes the road grade information to which the edge belongs.
Performing space query operation through the input point layer and the polygon layer to obtain a polygon where the point is as a starting point of search;
making the id of the original polygon be 0, and needing to perform an updating operation to make the id of the polygon searched from different starting points be the same;
iterative searching is carried out, wherein the searching termination condition of one edge is that the edge meeting the condition limit is searched or the boundary of the whole area is searched, and the step is carried out through a left polygon topological table and a right polygon topological table of a line;
outputting a search result and updating the face element id;
a3, post-treatment:
erasing the extracted area according to the input water system layer, and obtaining a final result layer after the layer in post-treatment is erased;
a4, output:
and outputting the compressed file of ShapeFile.
(2) The vector framing method comprises the following processing steps:
b1, extracting line segments with the same level according to the level of the line segments, and generating a bounding region by using a FeatureToPolygon tool. Generating a corresponding number of enclosing areas when the test line segments have several grades, wherein when the enclosing areas are generated according to different grades, not only the grade line is selected, but also the highest grade line is selected, and the two lines are enclosed together;
b2, respectively carrying out space query on the tested points and the enclosure areas with different levels generated in the step B1 to obtain the boundaries of the test points in the enclosure areas with different levels;
and B3, combining the obtained boundaries and outputting a result.
(3) The processing steps of the grid expanding method are as follows:
c1, extracting a certain grade line from the original road map layer to generate raster data as seed expansion;
c2, extracting coordinates of points of the input test point layer, calculating to obtain row and column numbers of the coordinates in the grid data, and taking the row and column numbers as seed growing points;
and C3, taking the 4-neighborhood as a search range, performing growth search by using the seed points in the step C2, wherein the termination condition of the search is that a boundary or a road with a corresponding grade is searched, the test point set comprises a plurality of points with different positions, the search range of each seed point is different, different raster files are output, and the raster files are converted into vector data.
The browser page is provided with an algorithm selection area, a function selection area, an operation selection area and a result display area, wherein the algorithm selection area is used for realizing area division by selecting different algorithms according to different input data formats; the function selection area is used for realizing algorithm input selection, data input selection and weight input selection; the operation selection area is provided with a submission button, a storage button and a clearing button, and the submission button is clicked after the user finishes filling the form; after the browser loads the region division result, the generated ShapeFile compressed file can be saved to any position by clicking the saving button; clicking the clear button may remove the region expansion results loaded on the map on the right side from the map base.
In order to compare and select the areas finally enclosed by different search weights, when the calculation principle involved in the system selects N levels for the search weights, the system calculates the enclosed areas of N-1, N-2, 1 and marked by different colors respectively, wherein N is an integer larger than 1. Specifically, when the search weight selects level 2, the system calculates the enclosed areas of level 2 and level 1 and respectively identifies the enclosed areas with different colors; when the search weight selects level 3, the system calculates the enclosed areas of level 3, level 2 and level 1 and respectively identifies the enclosed areas by different colors; and so on. The three algorithms all adopt the same color to mark the region division result of the corresponding grade, so as to facilitate the comparison of different algorithms. The region division results with the search weights of 1 level, 2 levels, 3 levels and 4 levels are respectively marked by red, green, yellow and blue.
And finally, inputting a water area layer, a water system line layer, a road layer with weights and a plurality of starting points in the range of the Chongqing city to test the operation effect of the system. During testing, the vector external expansion method and the grid external expansion method adopt 4-level search weights, and it can be seen that the enclosing results of 4 levels are marked by different colors; the vector framing method uses 2-level search weights, and only the red (corresponding to level 1) and green (corresponding to level 2) region division results are loaded according to the calculation principle described earlier. Meanwhile, different partition modes can be visually compared by switching the option cards of the first algorithm, the second algorithm and the third algorithm in the upper left corner of the graph 3, so that the optimal partition method can be selected. The operation of saving and deleting the current visual layer button is provided at the lowest end of the user system interface, so that the user can use the visual layer button conveniently.
The specific operation steps are as follows:
1) operation of vector extrapolation
Data file selection: by selecting the file button, the type of the input geographic feature element, such as a road, a water system, a POI, etc., is selected.
Weight level setting: and selecting corresponding weights for the input elements to carry out user-defined input characteristic region division.
Command submission: and starting to execute a proper amount of outward expansion algorithm to divide the characteristic area through a submit button.
Result file saving: and a save button for providing output result saving for the obtained result, and the user selects the saved position according to the situation.
2) Vector framing operation
The operation of the vector framing method is similar to that of a proper amount of external expansion method, and is divided into the following aspects:
data file selection: by selecting the file button, the type of the input geographic feature element, such as a road, a water system, a POI, etc., is selected.
Weight level setting: and selecting corresponding weights for the input elements to carry out user-defined input characteristic region division.
Command submission: and starting to execute a proper amount of outward expansion algorithm to divide the characteristic area through a submit button.
Result file saving: and a save button for providing output result saving for the obtained result, and the user selects the saved position according to the situation.
3) Operation of grid expanding method
Data file selection: by selecting the file button, the type of the input geographic feature element, such as a road, a water system, a POI, etc., is selected.
Weight level setting: and selecting corresponding weights for the input elements to carry out user-defined input characteristic region division.
Command submission: and starting to execute a proper amount of outward expansion algorithm to divide the characteristic area through a submit button.
And (4) saving a result file: and a save button for providing output result saving for the obtained result, and the user selects the saved position according to the situation.
After the corresponding left form is filled in and the three algorithms are operated, the final effects displayed on the interface are respectively shown in fig. 4-6, and from the actual operation effect of the system in regional division in Chongqing, the automatic urban spatial feature region division system in the embodiment can realize reasonable division of the feature regions around the target point. From the practical operation of the three algorithms, the calculation speed of the algorithm one (i.e. the vector extrapolation method) is significantly slower than that of the other various cases when the search weight is calculated to be level 1, which is related to the calculation amount of the vector extrapolation method itself, and when the search weight is level 1 (highest level), more geographical elements need to be operated geographically.
For the grid expanding method, because the grid data cannot judge whether the test point falls into the bounding polygon, the point at the lower right corner continuously grows, and because the data is too large, the program automatically ends because the recursion is too deep. In order to enable the program, approximately the number of units of seed growing points is used as a limit, and when the growth exceeds a certain limit, the growth of the seed point is stopped.
The comparative analysis of the vector external expansion method and the grid external expansion method shows that the vector external expansion method has the advantages over the grid external expansion method that: (1) the influence of roads of different grades can be fully considered. The vector dilation method takes into account multiple rank lines to get a result, while the grid only takes into account one road rank. If the grid expanding method is used for considering a plurality of grades, the situation is complex in the actual treatment process, and the seed growth is difficult to judge. (2) The results obtained are more realistic (easier to find in comparison to the lower right hand corner of fig. 1 and 3) and the raster data accuracy is susceptible to resolution. And (3) the calculation of the grid method is influenced by the data volume of the grid.
The grid expanding method has the following advantages compared with the vector expanding method: (1) the process is simple and the algorithm is clear. (2) The speed is higher. The bottleneck limiting the computation speed of the vector extrapolation method is the element insertion step performed when the elements are stored as shp layers, that is, the more elements are inserted, the slower the speed is. The efficiency of the grid dilation method is also relatively high in terms of the algorithm calculation itself. The urban area can be reasonably divided based on the type data of water system, mountain, road, interest point (restaurant, supermarket, etc.) and the like input into the city. The division rules can be modified and controlled autonomously, and finally, the divided urban areas are displayed, so that the service of relevant managers such as the urban government is facilitated.
The system provides a set of method for automatically dividing characteristic areas and a visual sharing process of data results by utilizing various city planning data. In key technology, the method mainly has the following advantages in three aspects:
(1) three space calculation algorithms
Aiming at different input data formats, three algorithms are realized to realize the region division function, namely a vector expanding method, a grid expanding method and a vector frame determining method. And particularly, switching of different algorithms is carried out according to whether the data is vector data or raster data. And the method can support the input of various surface feature element types, and set different weight grades for each input element to carry out reasonable city area automatic division. Different algorithm selections, input element numbers, weight grades and the like can be carried out according to the preference and the requirement of a user, and different region division results are obtained by selecting different input interest point elements.
(2) Thin client application data sharing and visual analysis
Compared with the traditional desktop end for processing geographic information data, the system is developed based on a client B/S framework, can output the result after the division of the area to the user according to the setting rule selected by the user, and can support the user to select the result under different settings. The outcome output is carried out based on the B/S architecture, and the returned result can be provided for users to download, visually display, spatially analyze and other services. In the aspect of visual analysis, the system can realize comparison and display aiming at the region segmentation results obtained by different algorithms, and is convenient for algorithm result comparison.
By embedding the three algorithms into a front-end and back-end interactive framework, a user can check the automatically divided feature areas on a map base map of a Web interface only by filling and submitting a form in a browser, and the searching efficiency of the feature areas in a city is greatly improved.
(3) Data cleansing
The system can preprocess different types of input data, and can clean and filter the data to solve the problems of unreasonable and error existing in the original data, and realize correct regular region division of urban region division results for the obtained standard data set.
Based on the system, in the future urban spatial feature area division work, the automatic division result of the area can be corrected to a certain extent by considering the combination of more types of urban planning data, such as POI data, DEM data and the like, so that the area division result which is more personalized and suitable for various planning requirements can be obtained.
The technical solution provided by the present invention is described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (9)
1. The automatic urban spatial feature area dividing system is characterized in that: including user, browser, web server and backstage server, wherein:
the user side is used for acquiring the form content filled by the user;
the browser is used for splicing the form contents into a long character string and transmitting the long character string to the background server along with the fact that a user clicks a submission button;
the network server is used for processing the request of the browser and returning a corresponding response;
the background server analyzes the received character string, initializes a construction function of the built-in algorithm by using the analyzed parameter, and executes geographical calculation in the background; after the calculation is finished, transmitting the position and the file name of the generated ShapeFile compression file to a browser;
the browser analyzes the received data, converts the ShapeFile compressed file into a GeoJson format, and superimposes the result calculated by the background server on the map base map on the right side of the browser page for display.
2. The automatic urban spatial feature area division system according to claim 1, wherein: and the background server and the browser communicate by adopting a WebSocket protocol.
3. The automatic urban spatial feature area division system according to claim 1, wherein: the built-in algorithm of the background server comprises a vector external expansion method, a vector frame determination method and a grid external expansion method.
4. The automatic urban spatial feature area division system according to claim 3, wherein: the vector external expansion method comprises the following calculation processing steps:
a1, preprocessing data:
generating a road surface map layer through the road line map layer;
generating coverage data by the road surface map layer to obtain a topological relation;
extracting a line vector and a plane vector with a topological relation from the generated coverage data;
spatial connection, and mapping to obtain a line vector with weight attributes;
a2, merging regions:
obtaining the topological relation between the edge and the polygon;
performing space query operation through the input point layer and the polygon layer to obtain a polygon where the point is as a starting point of search;
enabling the id in the attribute of the original polygon to be 0, and performing updating operation to enable the id of the polygon searched from different starting points to be the same;
iterative search is carried out;
outputting a search result and updating the face element id;
a3, post-treatment:
erasing the extracted area according to the input water system layer, and obtaining a final result layer after the layer in post-treatment is erased;
a4, output:
and outputting the compressed file of ShapeFile.
5. The automatic urban spatial feature area division system according to claim 3, wherein: the vector framing method comprises the following processing steps:
b1, extracting line segments with the same grade according to the grade of the line segments, and generating a bounding region by using a FeatureToPolygon tool;
b2, respectively carrying out space query on the tested points and the enclosure areas with different levels generated in the step B1 to obtain the boundaries of the test points in the enclosure areas with different levels;
and B3, combining the obtained boundaries and outputting a result.
6. The automatic urban spatial feature area division system according to claim 3, wherein: the processing steps of the grid expanding method are as follows:
c1, extracting a certain grade line from the original road map layer to generate raster data as seed expansion;
c2, extracting coordinates of points of the input test point layer, calculating to obtain row and column numbers of the coordinates in the grid data, and taking the row and column numbers as seed growing points;
and C3, taking the 4-neighborhood as a search range, carrying out growth search by using the seed points in the step C2, outputting different raster files when the boundary or the road with the corresponding level is searched for at the end condition of the search, and converting the raster files into vector data.
7. The automatic urban spatial feature area division system according to any one of claims 3 to 6, wherein: the vector external expansion method, the vector frame determination method and the grid external expansion method are input by search weight, point layer, line layer, surface layer and corresponding weight.
8. The automatic urban spatial feature area division system according to claim 7, wherein: when the search weight selects N levels, the system calculates the enclosed areas of N-1, N-2, 1, and respectively identifies the enclosed areas with different colors, wherein N is an integer larger than 1.
9. The automatic urban spatial feature area division system according to claim 1, wherein: the interface of the browser is provided with an algorithm selection area, a function selection area, an operation selection area and a result display area, wherein the algorithm selection area is used for realizing area division by selecting different algorithms according to different input data formats; the function selection area is used for realizing algorithm input selection, data input selection and weight input selection; the operation selection area is provided with a submission button, a storage button and a clearing button, and the submission button is clicked after the user finishes filling the form; after the browser loads the region division result, the generated ShapeFile compressed file can be saved to any position by clicking the saving button; clicking the clear button may remove the region expansion results loaded on the map on the right side from the map base.
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