OD data drawing method

Abstract

The invention discloses an OD data drawing method, and relates to the technical fields of smart cities, urban planning, urban traffic and the like, and the method comprises the steps: constructing OD data with a grid as a basic unit; secondly, drawing a region of interest, solving a starting point or terminal point set of OD data in the region of interest, and performing full data processing on the starting point or terminal point set to obtain a starting point or terminal point set of the processed OD data; thirdly, point density space clustering is carried out on a starting point or ending point set of the processed OD data; according to a point density space clustering result, calculating a central point and an OD total number value of each point density space cluster; finally, OD line drawing is conducted on the center point of the region of interest and the center point of each point density space cluster on the map, and the number value of the OD total number value is displayed; or performing point grading display on the center point of each point density space cluster on the map according to the number value of the OD total number value. According to the method, OD data drawing can be quickly and effectively carried out.

Description

Technical Field

[0001] The present invention relates to the technical fields of smart cities, urban planning, urban transportation, etc., and particularly to a method for drawing OD data. Background Art

[0002] The drawing of OD data plays a very important role in the fields of urban planning, transportation planning, smart cities, geographic information systems, etc. OD data is a common type of data expressing the connection value between two entities in space. The existing OD data drawing methods mostly adopt the method of connecting all starting points and ending points for drawing. However, WEB online OD data drawing often faces problems such as a large amount of data and difficulty in displaying key information through complex OD graphic information. To a large extent, WEB online OD data analysis faces the problem of how to quickly extract key information dynamically from a large amount of OD data and be able to quickly draw it on the web page.

[0003] After analysis, it is found that the existing methods mainly focus on the spatial clustering of OD data expressed in line type to meet this practical need. However, line spatial clustering not only takes time but also cannot truly reveal the spatial connection relationship of OD data. Therefore, to solve this problem, it should be converted to the spatial distribution characteristics of the set of points associated with the starting point or ending point of OD data. 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 a method for drawing OD data. The present invention can quickly and effectively draw OD data on a large spatio-temporal scale and reveal the key information behind the large amount of OD data.

[0005] The present invention adopts the following technical solutions to solve the above technical problems: A method for drawing OD data according to the present invention includes: Step 1: For the entire study area, construct OD data with grids as basic units and calculate the OD connection values between the grids; Step 2: Draw an area of interest from the study area, solve the set of starting points or ending points of the OD data within the area of interest, and perform full-volume data processing on the set of starting points or ending points to obtain the set of starting points or ending points of the processed OD data; wherein, full-volume data processing refers to merging the values of the same grids in the set of starting points or ending points; Step 3: Perform point density spatial clustering on the set of starting points or ending points of the OD data processed in Step 2 to obtain a point density spatial clustering result; Step 4: Calculate the center point of each point density spatial clustering and the total OD value according to the point density spatial clustering result and the OD connection values between the grids; Step 5: Draw OD lines on the map for the center point of the region of interest and the center points of each point density spatial clustering, and display the numerical value of the total OD value; Or, display the point grading on the map for the center points of each point density spatial clustering according to the magnitude of the numerical value of the total OD value.

[0006] As a further optimization scheme of the OD data drawing method described in the present invention, in Step 2, after performing full-volume data processing on the starting point or end point set, simplified data processing is further performed to obtain the starting point or end point set of the processed OD data; wherein, the simplified data processing means: after merging the numerical values of the same grids in the starting point or end point set, taking the numerical values of all grids as a sequence, and by calculating the quantiles of the sequence, presetting a quantile threshold, and deleting the data below the quantile threshold.

[0007] As a further optimization scheme of the OD data drawing method described in the present invention, perform point density spatial clustering on the starting point or end point set of the OD data processed through Step 2; it is to cluster the geometric center points of the grids in the starting point or end point set.

[0008] As a further optimization scheme of the OD data drawing method described in the present invention, in Step 3, the method adopted for point density spatial clustering is DBSCN clustering, and when performing DBSCN clustering processing, a clustering distance value parameter is preset; Set the corresponding relationship between the number of grids in the starting point or end point set of the OD data and the clustering distance, that is, different numbers of grids to be clustered correspond to a clustering distance value.

[0009] As a further optimization scheme of the OD data drawing method described in the present invention, in Step 4, two methods are used to calculate the center point of each point density spatial clustering; wherein, The first method: Take the geometric center point of each point density spatial clustering result as the center point; The second method: Take the point with the maximum kernel density of the grids included in each point density spatial clustering result as the center point.

[0010] As a further optimization scheme of the OD data drawing method described in the present invention, the total OD value of each point density spatial clustering in Step 4 is the sum of the OD connection values between the grids included in each clustering result and the region of interest.

[0011] As a further optimization scheme of the OD data drawing method described in the present invention, in Step 5, the methods for obtaining the center point of the region of interest include Method 1 and Method 2, specifically as follows: Method 1: Take the geometric center point of the region of interest as the center point; Method 2: Take the point with the maximum kernel density value in the set of starting or ending points in the region of interest as the center point.

[0012] As a further optimization scheme of the OD data drawing method described in the present invention, the numerical value of the total OD value shown in step 5 is located at one end of the OD line.

[0013] As a further optimization scheme of the OD data drawing method described in the present invention, step 1 includes: Step 1.1: Divide the entire research area into grids of equal size; Step 1.2: Calculate the OD connection value between the grids in the research area through mobile positioning big data.

[0014] As a further optimization scheme of the OD data drawing method described in the present invention, the region of interest in step 2 is circular, rectangular or polygonal.

[0015] Compared with the prior art by adopting the above technical solutions, the present invention has the following technical effects: (1) The present invention provides an OD data drawing method, which can quickly and effectively draw OD data and reveal the key information behind a large amount of OD data; (2) Based on the compression algorithm of point type spatial data and the principle of cluster analysis, the present invention proposes an online, fast and flexible OD data drawing method. Description of the Drawings

[0016] Figure 1 is a schematic diagram of the overall process of the present invention.

[0017] Figure 2 is a schematic diagram of the relationship of one OD data.

[0018] Figure 3 is a schematic diagram of the relationship of OD data between multiple grids.

[0019] Figure 4 is a schematic diagram of the grid distribution with OD data relationship.

[0020] Figure 5 is a schematic diagram of the OD data drawing result.

[0021] Figure 6 is a schematic diagram of the map display result of the OD data drawing. Detailed Embodiments

[0022] The technical solutions of the present invention will be further described in detail below with reference to the drawings: To better adapt to the self-adaptive drawing of OD data, first, a faster OD data drawing method needs to be designed. Second, the number of lines finally displayed by the OD data should be appropriate and can be drawn by a general computer, reaching a relatively high level of self-adaptive OD data display for convenient actual production use. For this purpose, on the one hand, this invention will improve the drawing efficiency of OD data through certain methods. On the other hand, a faster and better OD data drawing method also needs to be provided, which can reasonably control the clustering results according to the data volume of OD data.

[0023] Step 1. Refer to the appendix Figure 1 , first, for the entire study area, construct OD data with grids as the basic units and calculate the OD connection values between grids. This step is the basis for generating the drawing of OD data and will form the travel OD relationship between grids of equal size in the study area. The specific steps are as follows: Step 1.1. Divide the entire study area into grids of equal size. This is a very beneficial processing scheme in practice, which can enable OD analysis of the entire study area according to a unified size.

[0024] Step 1.2. Calculate the OD connection values between grids in the study area through mobile positioning big data. Through the analysis of mobile positioning big data over a certain period, the OD connection values between grids can be solved. Refer to the appendix Figure 2 , and the connection values here can be the travel relationships between people or commuting travel relationships, which can reflect the population connection level between one grid and another grid.

[0025] Step 2. Draw the area of interest from the study area, solve the set of starting points or ending points of the OD data within the area of interest, and perform full-volume data processing on the set of starting points or ending points to obtain the set of starting points or ending points of the processed OD data; among them, full-volume data processing refers to merging the values of the same grids in the set of starting points or ending points; refer to the appendix Figure 3 , for grid B in the figure, it is necessary to merge its related set of starting points (grid A and grid D) so that grid A and grid D form a set (denoted as set b). The advantage of this processing is that for grid B, only the relationship between grid B and set b needs to be concerned, that is, only the spatial distribution relationship of the grids in set b needs to be concerned in space. That is to say, for the OD departure relationship of grid B, only the spatial aggregation point of set b needs to be calculated, and the connection value between set b and grid B needs to be given in space. From the innovative idea of algorithm design, this step is to make the complex OD line clustering processing become point-type data clustering processing, and can better ensure the scientific nature of the analysis results in space.

[0026] According to the above analysis, point density spatial clustering can be performed on the starting point or end point set of the OD data processed through this step; it is to perform clustering on the geometric center points of the grids in the starting point or end point set.

[0027] In this step, after performing full-volume data processing on the starting point or end point set, simplified data processing is also performed to obtain the starting point or end point set of the processed OD data; among them, simplified data processing refers to: after merging the values of the same grids in the starting point or end point set, taking the values of all grids as a sequence, presetting a quantile threshold by calculating the quantiles of the sequence, and deleting the data below the quantile threshold. In addition, it should be noted that the region of interest in this step is circular, rectangular or polygonal. It can be directly drawn by the user on the map.

[0028] 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. See the appendix Figure 4 , which shows the relationship between grid X and the relevant grid OD connection grids in the figure. As an example, it is not difficult to find that grid X has a strong connection with Figure 4 two places in (the aggregation places of grids a1, a2, a3, a4, a5, a6, a7 and the aggregation places of grids b1, b2, b3, b4, b5, b6, b7, b8 respectively). Then, the specific method for point density spatial clustering is to take the geometric center points of grids a1, a2, a3, a4, a5, a6, a7, b1, b2, b3, b4, b5, b6, b7, b8 for point density spatial clustering.

[0029] In this step, the method used for point density spatial clustering is DBSCN clustering. When performing DBSCN clustering processing, the clustering distance value parameter is preset; the corresponding relationship between the number of grids in the starting point or end point set of the OD data and the clustering distance is set, that is, different numbers of grids to be clustered correspond to a clustering distance value. The advantage of this step of processing is that a certain adaptive clustering processing effect can be achieved. Because the corresponding relationship between the number of grids and the clustering distance is set, then in the actual DBSCN clustering processing, the appropriate clustering distance value parameter can be automatically selected, and thus the rationality of the clustering result in the spatial distribution can be guaranteed automatically without the need for complex clustering parameter settings by humans.

[0030] Step 4: Calculate the center point and the total OD value of each point density spatial clustering according to the point density spatial clustering result and the OD connection value between grids; In this step, two methods are used to calculate the center point of each point density spatial clustering; among them, The first method: Take the geometric center point of each point density spatial clustering result as the center point; The second method: Take the point with the maximum kernel density of the grid included in each point density spatial clustering result as the center point.

[0031] In this step, the total OD value of each point density spatial clustering is the sum of the OD connection values between the grids included in each clustering result and the region of interest.

[0032] See Appendix Figure 5 , the clustering result shows the OD connection relationship of grid X in space, that is, transforms the complex OD connection relationship in Figure 4 into the OD connection relationship between grid X, grid a and grid b. It greatly reduces the original complex grid connection distribution and provides a clearer display of the OD line relationship for practical applications.

[0033] Step 5: Draw OD lines on the map between the center point of the region of interest and the center points of each point density spatial clustering, and display the numerical value of the total OD value; In this step, the method for obtaining the center point of the region of interest is as follows: Method 1: Take the geometric center point of the region of interest as the center point; Method 2: Take the point with the maximum kernel density of the starting point or ending point set within the region of interest as the center point.

[0034] The numerical value of the total OD value displayed in this step is located at one end of the OD line. Or, perform point grading display on the map for the center points of each point density spatial clustering according to the magnitude of the numerical value of the total OD value.

[0035] See Appendix Figure 6 , a specific system platform implementation case of the present invention. Users can draw a region of interest on the map, and then quickly cluster to obtain the OD lines of interest and the total OD values of each connected OD line.

[0036] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for plotting OD data, characterized in that, Including: Step 1: For the entire study area, construct OD data with grids as basic units, and calculate the OD connection values between grids. Step 2: Draw the area of interest from the study area, solve the set of starting points or ending points of the OD data within the area of interest, and perform full - volume data processing on the set of starting points or ending points to obtain the set of starting points or ending points of the processed OD data. Among them, full - volume data processing refers to merging the values of the same grids in the set of starting points or ending points. Step 3: Perform point - density spatial clustering on the set of starting points or ending points of the OD data processed in Step 2 to obtain the point - density spatial clustering result. Step 4: According to the point - density spatial clustering result and the OD connection values between grids, calculate the center point of each point - density spatial clustering and the total OD value. Step 5: Draw OD lines on the map between the center point of the area of interest and the center point of each point - density spatial clustering, and display the numerical value of the total OD value. Or, perform point - grading display on the map for the center points of each point - density spatial clustering according to the magnitude of the numerical value of the total OD value.

2. The OD data drawing method according to claim 1, characterized in that In Step 2, after performing full - volume data processing on the set of starting points or ending points, simplified data processing is also performed to obtain the set of starting points or ending points of the processed OD data. Among them, simplified data processing means that after merging the values of the same grids in the set of starting points or ending points, the values of all grids are taken as a sequence, and by calculating the quantiles of the sequence, a quantile threshold is preset, and the data below this quantile threshold is deleted.

3. A method for drawing OD data according to claim 1, characterized in that Performing point - density spatial clustering on the set of starting points or ending points of the OD data processed in Step 2 is to cluster the geometric center points of the grids in the set of starting points or ending points.

4. A method for drawing OD data according to claim 1, characterized in that, In Step 3, the method adopted for point - density spatial clustering is DBSCN clustering. When performing DBSCN clustering processing, a clustering distance value parameter is preset in advance. Set the corresponding relationship between the number of grids in the set of starting points or ending points of the OD data and the clustering distance, that is, different numbers of grids to be clustered correspond to a clustering distance value.

5. A method for drawing OD data according to claim 1, characterized in that, In Step 4, two methods are used to calculate the center point of each point - density spatial clustering. Among them, The first method: Take the geometric center point of each point - density spatial clustering result as the center point. The second method: Take the point with the maximum kernel density of the grids included in each point - density spatial clustering result 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 clustering in Step 4 is the sum of the OD connection values between the grids included in each clustering result and the area of interest.

7. A method for drawing OD data according to claim 1, characterized in that, In Step 5, the methods for obtaining the center point of the area of interest include Method 1 and Method 2, specifically 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 set of starting points or ending points within the area of interest as the center point.

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

9. A method for drawing OD data 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 values between the grids in the study area through mobile positioning big data.

10. A method for drawing OD data according to claim 1, characterized in that, In Step 2, the area of interest is circular, rectangular or polygonal.

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