A method for rendering od data

Abstract

The application discloses an OD data plotting method, and relates to the technical fields of smart city, city planning, city traffic and the like. First, OD data is constructed with a grid as a basic unit; second, a region of interest is plotted, a start point or end point set of the OD data in the region of interest is solved, and full data processing is performed on the start point or end point set to obtain a start point or end point set of processed OD data; third, the start point or end point set of the processed OD data is subjected to point density spatial clustering; according to the point density spatial clustering result, a center point of each point density spatial cluster and an OD total value are calculated; finally, OD lines are plotted on a map for the center point of the region of interest and the center point of each point density spatial cluster, and a quantity value of the OD total value is displayed; or, the center point of each point density spatial cluster is subjected to point hierarchical display on the map according to the quantity value of the OD total value.

Description

Technical Field

[0001] The present invention relates to the technical fields of smart cities, urban planning, urban transportation, and the like, and in particular to an OD data drawing method. Background Art

[0002] OD data rendering plays a crucial role in urban planning, transportation planning, smart cities, geographic information systems, and other fields. OD data is a common form of data that expresses the relationship between two entities in space. Existing OD data rendering methods often use lines connecting all starting and ending points. However, web-based OD data rendering often faces challenges such as large data volumes and the difficulty of displaying key information through complex OD graphics. To a large extent, web-based OD data analysis faces the challenge of rapidly and dynamically extracting key information from large amounts of OD data, while also requiring rapid rendering on the web page.

[0003] After analysis, we found that existing methods primarily focus on spatial clustering of OD data represented by line types to meet this practical need. However, line spatial clustering is not only time-consuming but also fails to truly reveal the spatial connections between OD data. Therefore, to solve this problem, it is necessary to convert the spatial distribution characteristics of the points associated with the starting or ending points of the OD data into spatial distribution characteristics. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide an OD data rendering method. The present invention can quickly and effectively render large-scale spatiotemporal OD data and reveal the key information behind large amounts of OD data.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] An OD data drawing method proposed in the present invention includes:

[0007] Step 1: For the entire study area, construct OD data with grids as the basic unit and calculate the OD connection value between grids;

[0008] Step 2: Draw an area of ​​interest from the study area, find the starting point or end point set of the OD data within the area of ​​interest, and perform full data processing on the starting point or end point set to obtain the processed starting point or end point set of the OD data; full data processing refers to merging the values ​​of the same grid in the starting point or end point set;

[0009] Step 3: Perform point density spatial clustering on the starting point or end point set of the OD data processed in step 2 to obtain the point density spatial clustering result;

[0010] Step 4, according to the point density spatial clustering result and the OD connection value between grids, the center point of each point density spatial clustering and the OD total value are calculated;

[0011] Step 5, the OD line drawing is performed on the map between the center point of the region of interest and the center point of each point density spatial clustering, and the quantity value of the OD total value is displayed;

[0012] Or, the center point of each point density spatial clustering is displayed on the map according to the quantity value of the OD total value.

[0013] As a further optimization scheme of the OD data drawing method, in step 2, after the full data processing of the origin or destination set, the simplified data processing is further performed to obtain the processed origin or destination set of the OD data; wherein the simplified data processing refers to: after merging the values of the same grid in the origin or destination set, all the grid values are taken as a sequence, the quantile of the sequence is calculated, a quantile threshold is preset, and the data lower than the quantile threshold is deleted.

[0014] As a further optimization scheme of the OD data drawing method, the point density spatial clustering is performed on the origin or destination set of the OD data processed in step 2; which is to cluster the geometric center points of the grids in the origin or destination set.

[0015] As a further optimization scheme of the OD data drawing method, in step 3, the method used for the point density spatial clustering is DBSCN clustering, and a clustering distance value parameter is preset when the DBSCN clustering is processed.

[0016] The corresponding relationship between the grid quantity in the origin or destination set of the OD data and the clustering distance is set, that is, different grid quantities to be clustered correspond to a clustering distance value.

[0017] As a further optimization scheme of the OD data drawing method, in step 4, two methods are used to calculate the center point of each point density spatial clustering; wherein,

[0018] The first method:

[0019] The geometric center point of each point density spatial clustering result is taken as the center point;

[0020] The second method:

[0021] The kernel density maximum point of the grid included in each point density spatial clustering result is taken as the center point.

[0022] As a further optimization scheme of the OD data plotting method, the OD total value of each point density space clustering in step 4 is the sum of the OD connection values between the grid included in each clustering result and the region of interest.

[0023] As a further optimization scheme of the OD data plotting method, in step 5, the method for obtaining the center point of the region of interest includes method one and method two, and the details are as follows.

[0024] Method one: taking the geometric center point of the region of interest as the center point;

[0025] Method two: taking the kernel density maximum point of the origin or terminal point set in the region of interest as the center point.

[0026] As a further optimization scheme of the OD data plotting method, the number value of the OD total value displayed in step 5 is located at one end of the OD line.

[0027] As a further optimization scheme of the OD data plotting method, step 1 includes:

[0028] Step 1.1, dividing the entire study area into grids of equal size;

[0029] Step 1.2, calculating the OD connection values between the grids in the study area through mobile positioning big data.

[0030] As a further optimization scheme of the OD data plotting method, the region of interest in step 2 is circular, rectangular or polygonal.

[0031] Compared with the prior art, the technical scheme of the present application has the following technical effects:

[0032] (1) The present application provides an OD data plotting method, which can quickly and effectively plot OD data and reveal the key information behind large amounts of OD data.

[0033] (2) The present application proposes an online fast and flexible OD data plotting method based on the compression algorithm of point type spatial data and the clustering analysis principle. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is the overall flowchart of the present application.

[0035] Figure 2 is a schematic diagram of an OD data relationship.

[0036] Figure 3 is a schematic diagram of the OD data relationship between multiple grids.

[0037] Figure 4 is a schematic diagram of grid distribution with OD data relationship.

[0038] Figure 5 is a schematic diagram of OD data plotting result.

[0039] Figure 6 is a schematic diagram of OD data plotting map display result. DETAILED DESCRIPTION

[0040] The technical solutions of the present application will be further described in detail below in combination with the drawings:

[0041] To better adapt to the adaptive plotting of OD data, a faster OD data plotting method needs to be designed first. Secondly, the number of lines finally displayed by the OD data needs to be appropriate, which can be plotted by a general computer to achieve a high adaptive OD data display level, facilitating actual production and use. Therefore, the present application will improve the plotting efficiency of OD data through certain methods on one hand. On the other hand, a faster and better plotting method of OD data needs to be provided to reasonably control the clustering results according to the data size of OD data.

[0042] Step 1, participate in the attached Figure 1 Firstly, the OD data of the entire study area is constructed with grid as the basic unit, and the OD contact value between grids is calculated. This step is the basis for generating the OD data plotting, which will form the OD relationship between grids of the same size in the study area. The specific steps are as follows:

[0043] Step 1.1, divide the entire study area into grids of the same size. This is a very beneficial processing scheme in practice, which can make the entire study area be analyzed according to the uniform size.

[0044] Step 1.2, calculate the OD contact value between grids in the study area through mobile positioning big data. Through the analysis of mobile positioning big data in a certain period, the OD contact value between grids can be solved. Participate in the attached Figure 2 The contact value here can be the travel relationship between the crowd, or the commuting travel relationship, which can reflect the crowd contact level between one grid and another grid.

[0045] Step 2, plot the area of interest from the study area, solve the start point or end point set of the OD data in the area of interest, and perform full data processing on the start point or end point set to obtain the processed start point or end point set of the OD data; wherein the full data processing refers to merging the same grid values in the start point or end point set; participate in the attached Figure 3For grid B in the figure, its related starting point sets (grid A and grid D) need to be merged so that grid A and grid D are combined as a set (denoted as set b). The advantage of this approach is that for grid B, we only need to care about the relationship between grid B and set b, that is, we only need to care about the spatial distribution relationship of the grids in set b. In other words, the OD starting relationship of grid B only requires calculating the spatial aggregation point of set b and spatially giving the connection value between set b and grid B. From the perspective of innovative algorithm design, this step is to transform the complex OD line clustering processing into the clustering processing of point-type data, and to better ensure the scientific nature of the analysis results in space.

[0046] Based on the above analysis, point density spatial clustering can be performed on the starting point or end point set of the OD data processed in this step; that is, clustering can be performed on the geometric center points of the grids in the starting point or end point set.

[0047] In this step, after processing the full data set for the start or end point, simplified data processing is performed to obtain the processed start or end point data set. Simplified data processing involves merging the values ​​of the same grid within the start or end point set, treating all grid values ​​as a sequence, calculating the quantile of the sequence, and presetting a quantile threshold. Data below this threshold is then deleted. It is important to note that the area of ​​interest in this step is a circle, rectangle, or polygon. This can be drawn directly on the map by the user.

[0048] Step 3: Perform point density spatial clustering on the starting point or end point set of the OD data processed in step 2 to obtain the point density spatial clustering result. Figure 4 , showing the relationship between grid X and the related grid OD contact grid in the figure. As an example, it is not difficult to find that grid X and Figure 4 There are two points (the cluster of grids a1, a2, a3, a4, a5, a6, and a7 and the cluster of grids b1, b2, b3, b4, b5, b6, b7, and b8) that have strong connections. Therefore, the specific point density spatial clustering method is to take the geometric center points of grids a1, a2, a3, a4, a5, a6, a7, b1, b2, b3, b4, b5, b6, b7, and b8 and perform point density spatial clustering.

[0049] In this step, the point density spatial clustering method used is DBSCN clustering. During the DBSCN clustering process, the cluster distance value parameter is pre-set; a correspondence is set between the number of grid cells in the start or end point set of the OD data and the cluster distance, that is, each different number of grid cells to be clustered corresponds to a cluster distance value. The advantage of this step is that it can achieve a certain degree of adaptive clustering effect. Because the correspondence between the number of grid cells and the cluster distance is set, the appropriate cluster distance value parameter can be automatically selected during the actual DBSCN clustering process, thereby ensuring the rationality of the spatial distribution of the clustering results, without the need for manual and complex clustering parameter settings.

[0050] Step 4: Calculate the center point of each point density spatial cluster and the total OD value based on the point density spatial clustering results and the OD connection value between grids;

[0051] In this step, two methods are used to calculate the center point of each point density spatial cluster;

[0052] First method:

[0053] Take the geometric center point of each point density spatial clustering result as the center point;

[0054] Second method:

[0055] The point with the maximum kernel density of the grid included in each point density spatial clustering result is taken as the center point.

[0056] The total OD value of each point density spatial cluster in this step is the sum of the OD connection values ​​between the grids included in each clustering result and the area of ​​interest.

[0057] See attached Figure 5 The clustering results show the OD relationship of grid X in space, that is, Figure 4 The complex OD relationship in the grid is transformed into the OD relationship between grid X and grids a and b. This greatly reduces the original complex grid connection distribution and provides a clearer OD line relationship display for practical applications.

[0058] Step 5: Draw OD lines on the map for the center point of the area of ​​interest and the center point of each point density spatial cluster, and display the total OD value;

[0059] In this step, the method for obtaining the center point of the region of interest is as follows:

[0060] Method 1: Take the geometric center point of the area of ​​interest as the center point;

[0061] Method 2: Take the point with the maximum kernel density of the starting point or end point set in the area of ​​interest as the center point.

[0062] The OD total value displayed in this step is located at one end of the OD line. Alternatively, the center point of each point density spatial cluster is displayed on the map in a hierarchical manner according to the OD total value.

[0063] See attached Figure 6 In a specific system platform implementation case of the present invention, the user can draw an area of ​​interest on the map, and then quickly cluster the OD lines of interest and obtain the total OD value of each connected OD line.

[0064] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for plotting OD data, characterized in that: include: Step 1: For the entire study area, construct OD data with grids as the basic unit and calculate the OD connection value between grids; Step 2: Draw an area of ​​interest from the study area, find the start or end point set of the OD data within the area of ​​interest, and perform full data processing on the start or end point set to obtain the processed start or end point set of the OD data; full data processing refers to merging grids with the same start or end point; Step 3: Perform point density spatial clustering on the starting point or end point set of the OD data processed in step 2 to obtain the point density spatial clustering result; Step 4: Calculate the center point of each point density spatial cluster and the total OD value based on the point density spatial clustering results and the OD connection value between grids; Step 5: Draw OD lines on the map for the center point of the area of ​​interest and the center point of each point density spatial cluster, and display the total OD value; Alternatively, the center point of each point density spatial cluster can be displayed on the map in a hierarchical manner according to the size of the total OD value.

2. The OD data drawing method according to claim 1, characterized in that: In step 2, after full data processing of the starting point or end point set, simplified data processing is also performed to obtain the starting point or end point set of processed OD data; wherein, simplified data processing means: after merging grids with the same starting point or end point, the values ​​of all grids are taken as a sequence, and by calculating the quantile of the sequence, a quantile threshold is preset, and the data below the quantile threshold is deleted.

3. The OD data drawing method according to claim 1, characterized in that: For the starting point or end point set of the OD data processed in step 2, point density spatial clustering is performed: the geometric center points of the grids in the starting point or end point set are clustered.

4. The OD data drawing method according to claim 1, characterized in that: In step 3, the method used for point density spatial clustering is DBSCN clustering. When performing DBSCN clustering processing, the cluster distance value parameter is pre-set; Set the correspondence between the number of grids in the start or end point set of OD data and the clustering distance, that is, the number of different grids to be clustered corresponds to one clustering distance value.

5. The OD data drawing method according to claim 1, characterized in that: In step 4, two methods are used to calculate the center point of each point density spatial cluster; First method: Take the geometric center point of each point density spatial clustering result as the center point; Second method: The point with the maximum kernel density of the grid included in each point density spatial clustering result is taken as the center point.

6. The OD data drawing method according to claim 1, characterized in that: The total OD value of each point density spatial cluster in step 4 is the sum of the OD connection values ​​between the grids and the area of ​​interest included in each clustering result.

7. The OD data drawing method according to claim 1, characterized in that: In step 5, the method for obtaining the center point of the area of ​​interest includes method 1 and method 2, which are as follows: Method 1: Take the geometric center point of the area of ​​interest as the center point; Method 2: Take the point with the maximum kernel density of the starting point or end point set in the area of ​​interest as the center point.

8. The OD data drawing method according to claim 1, characterized in that: The total OD value displayed in step 5 is located at one end of the OD line.

9. The OD data drawing method according to claim 1, characterized in that: Step 1 includes: Step 1.1: Divide the entire study area into grids of equal size; Step 1.2: Calculate the OD connection value between grids in the study area through mobile positioning big data.

10. The OD data drawing method according to claim 1, characterized in that: In step 2, the region of interest is a circle, rectangle or polygon.

Images