Trajectory visualization method based on hierarchical location information embedded with timeline

Abstract

The present invention relates to a trajectory visualization method based on hierarchical location information embedded with a timeline. The method comprises: extracting hierarchically structured descriptive trajectory data from a text description to be analyzed, for two information elements, i.e., time and space, encoding same by using horizontal and vertical positional dimensions respectively, and mapping each trajectory into a curve that changes spatially over time; using a layout optimization algorithm to perform curve layout optimization; and allowing locations, of which trajectory occurrence times do not overlap, under the same parent location to share a vertical positional interval, thereby obtaining a visual trajectory embedded with a timeline. By using the method disclosed in the present invention, the spatial proximity of different individuals at different times is presented to users in a manner similar to a story timeline; and by using the hierarchical abstract representation and adapting to the situation that spatial information in data has different description granularity, trajectory features can be objectively and effectively revealed, and a reliable foundation is laid for visualization development of the spatial information.

Description

A trajectory visualization method based on timeline with hierarchical location information embedded Technical Field

[0001] The present invention belongs to the field of visualization, and in particular relates to a trajectory visualization method based on a timeline with hierarchical location information embedded therein. Background Art

[0002] Trajectory data is an important data analysis object in many fields. In trajectory data analysis, analyzing the co-occurrence of multiple individual trajectories is an important research direction. However, when there is a high degree of uncertainty in the trajectory and co-occurrence data cannot be obtained directly, it is necessary to rely on external automated methods or analysts to participate in fuzzy speculation. For example, in an epidemiological survey of the activity trajectory of individual patients, only a hierarchical regional description of the patient's route can be obtained from the survey report. The data points in the trajectory are in the form of "Beijing - Haidian District - Yiheyuan Road - Hotel X" or "Hebei Province - Shijiazhuang City". In the subsequent description of this invention, with respect to geographic information description, trajectory data with such characteristics will be referred to as hierarchical descriptive trajectory.

[0003] Existing trajectory visualization methods are not well suited for analyzing hierarchical descriptive trajectories. Traditional trajectory visualization often relies on maps, but maps are not convenient for displaying location descriptions with different ambiguities in hierarchical descriptive trajectories. In addition, there are many trajectory visualizations that use abstract locations, such as flow graphs and spatiotemporal networks. However, these methods are not fully adapted to analyzing the co-occurrence of people and data in hierarchical descriptive trajectories, and therefore cannot efficiently and objectively extract trajectory features from the co-occurrence of hierarchical descriptive trajectories.

[0004] Summary of the Invention

[0005] In response to the shortcomings of the prior art, the present invention aims to provide a trajectory visualization method based on a timeline with hierarchical location information embedded in it, which is used to analyze the co-occurrence of hierarchically descriptive trajectories. Through visual encoding and visual layout calculation, the spatial proximity of different individuals at different times is presented to the user in a way similar to a story timeline. The use of a hierarchical abstract representation in the display of spatial information adapts to the situation where spatial information in the data has different description granularities, can objectively and effectively reveal trajectory characteristics, and lay a solid foundation for the development of spatial information visualization.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] A trajectory visualization method based on a timeline with hierarchical location information embedded therein comprises the following steps:

[0008] S1. Extract hierarchical descriptive trajectory data from the text description to be analyzed. The two information elements of time and space are encoded using the horizontal and vertical position dimensions respectively. Each trajectory will be mapped into a curve that changes in space along time.

[0009] S2. Setting an energy function for the optimization target of the curve and using a layout optimization algorithm to optimize the curve layout;

[0010] S3. Allowing locations with non-interfering trajectories under the same parent location to share the same vertical position interval to improve the encoding rules and obtain a visual trajectory embedded in the timeline.

[0011] Furthermore, encoding using the horizontal and vertical position dimensions in step S1 includes the following sub-steps:

[0012] S11. Use horizontal position to encode time information. The horizontal position is linearly mapped from left to right to the time range involved in the hierarchical descriptive trajectory data.

[0013] S12. Use vertical position to encode location information. The locations in the hierarchical descriptive trajectory data are organized into a location hierarchy tree according to administrative regions. Each location is mapped to an interval or a node on the vertical position.

[0014] Furthermore, the hierarchical relationship between the locations in step S1 is reflected through the relationship between the corresponding intervals.

[0015] Furthermore, if location A includes location B, the interval at the longitudinal position corresponding to location A also includes the interval corresponding to location B. If there is no inclusion relationship between the two locations, the intervals corresponding to the two locations do not overlap.

[0016] Furthermore, the optimization objective in step S2 includes at least one of minimizing the length of the trajectory curve, minimizing the number of trajectory curve intersections, and minimizing the total height of visualization.

[0017] Furthermore, the final energy function used in the simulated annealing in step S2 is the weighted sum of the energy functions corresponding to the optimization objectives: minimize E(x) = β1E length +β2E crossing +β3E white-space ,

[0018] Among them E length To minimize the energy function corresponding to the trajectory curve length; E crossing To minimize the energy function corresponding to the number of intersections of trajectory curves; E white-space Minimize the energy function corresponding to the total height of the visualization.

[0019] Furthermore, the energy function corresponding to the minimum trajectory curve length is calculated by the following function:

[0020] Furthermore, the energy function corresponding to the number of trajectory curve intersections is calculated by the following function: E crossing =#(trajectory crossing).

[0021] Furthermore, the energy function corresponding to the minimum total height of visualization is calculated by the following function: E white-space =Y max -Y min .

[0022] The beneficial technical effect of the present invention is that the disclosed trajectory visualization method based on a timeline with hierarchical location information embedded therein can accurately and effectively present information in hierarchical descriptive trajectory data and help users understand the co-occurrence of these trajectories at locations of different granularities. Through the coordination of visual encoding methods, layout optimization algorithms, and encoding improvements, it can produce visualizations with high spatial utilization and good readability, and can objectively and effectively reveal trajectory characteristics, laying a solid foundation for the development of spatial information visualization. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] FIG1 is a schematic diagram of a process of encoding location information in a vertical position using a trajectory visualization method based on a timeline with hierarchical location information embedded therein according to a first embodiment of the present invention;

[0024] FIG2 is a schematic diagram of a curve changing in space along time generated by a trajectory visualization method based on a timeline with hierarchical location information embedded therein according to the first embodiment of the present invention;

[0025] FIG3 is a schematic diagram of a curve layout optimization process using a layout optimization algorithm in a trajectory visualization method based on a timeline with hierarchical location information embedded therein according to the first embodiment of the present invention;

[0026] FIG4 is a schematic diagram of a process for improving encoding rules in a trajectory visualization method based on embedding hierarchical location information into a timeline according to the first embodiment of the present invention;

[0027] FIG5 is a visualized trajectory graph generated by a trajectory visualization method based on a timeline with hierarchical location information embedded therein according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0029] Example 1

[0030] An embodiment of the present invention provides a trajectory visualization method based on a timeline with hierarchical location information embedded therein, the method comprising the following steps:

[0031] S1. Extract hierarchical descriptive trajectory data from the text description to be analyzed. The two information elements of time and space are encoded using the horizontal and vertical position dimensions respectively. Each trajectory will be mapped into a curve that changes in space along time.

[0032] The specific encoding rules are:

[0033] S11. Use the horizontal position (x-axis) to encode time information, and the horizontal position is linearly mapped from left to right to the time range involved in the data.

[0034] S12. Use the vertical position (y-axis) to encode location information. The locations in the data are organized into a location hierarchy tree according to administrative regions. Each location is mapped to an interval or a node on the vertical position (y-axis).

[0035] As shown in Figure 1, hierarchical descriptive trajectory data is extracted from the text description to be analyzed. The geographical location in the data has different levels of uncertainty, and the geographical location of each record is a node on the location hierarchy tree.

[0036] As shown in Figure 2, time information is linearly encoded on the horizontal position (x-axis), and the location hierarchy tree representing location information is encoded on the vertical position (y-axis). Each location is mapped to an interval, and the parent-child relationship in the tree is reflected in the inclusion relationship of the interval. Under this encoding rule, the trajectory is mapped into a curve.

[0037] S2. Set an energy function for the optimization target of the curve, and use a layout optimization algorithm to optimize the curve layout.

[0038] The hierarchical relationship between locations is reflected through the relationship between corresponding intervals. If location A contains location B, then the interval on the vertical position (y-axis) corresponding to location A also contains the interval corresponding to location B. If there is no inclusion relationship between the two locations, then their corresponding intervals do not overlap.

[0039] Within these encoding rules, there is flexibility in the vertical placement (y-axis) of locations and the placement of trajectory curves. Intervals for sub-locations of the same parent location can be swapped, and the size of intervals can be adjusted. Therefore, the specific placement of trajectory curves can also be adjusted within the corresponding intervals. This flexibility allows different drawing methods to express the same semantics but produce different presentations.

[0040] To this end, in an embodiment of the present invention, a layout optimization algorithm is used to improve space utilization and reduce visual interference. In the layout optimization algorithm, several optimization objectives are proposed, and energy functions are set for these optimization objectives. The simulated annealing algorithm is used for optimization. Specifically, the optimization objectives include:

[0041] (1) Minimizing the length of trajectory curves: The curve of a trajectory should not have too much curvature and should be kept as straight as possible. In the embodiment of the present invention, the curve length is used to measure the degree of curvature, and the length of each trajectory curve is minimized as much as possible. The energy function corresponding to minimizing the length of the trajectory curve is:

[0042] (2) Minimize the number of trajectory curve intersections: The intersections between trajectory curves will interfere with the user's reading and understanding of information from different trajectory curves, so during optimization, we hope to minimize the number of trajectory curve intersections. The energy function corresponding to minimizing the number of trajectory curve intersections is: E crossing =#(trajectory crossing)

[0043] (3) Minimize the total height of visualization: The final visualization space utilization rate is required to be as high as possible, and more information is presented compactly. Since the horizontal position (x-axis) direction is mapped to a fixed time, the optimization goal only needs to minimize the total height of visualization. The energy function corresponding to minimizing the total height of visualization is: E white-space =Y max -Y min

[0044] The final energy function used in simulated annealing is the weighted sum of the above three: minimize E(x) = β1E length +β2E crossing +β3E white-space

[0045] As shown in Figure 3, the layout optimization algorithm uses simulated annealing, and the three optimization objectives are expressed as three energy functions respectively. The optimization objectives include minimizing the trajectory curve length, minimizing the trajectory curve intersection, and minimizing the total visualization height.

[0046] S3. Allowing locations with non-interfering trajectories under the same parent location to share a longitudinal position (y-axis) interval to improve the encoding rule and obtain a visual trajectory embedded in the timeline.

[0047] In real-world data, many locations are only visited by a small number of trajectories at rare times, resulting in low spatial utilization. Allowing these infrequently used locations to share the same y-axis interval can significantly improve visualization space utilization while minimizing the impact on user comprehension. To this end, we propose a location packing optimization strategy that allows locations whose trajectories under the same parent location do not interfere with each other to share the same vertical position (y-axis) interval.

[0048] As shown in Figure 4, under the original encoding rules, many locations only have trajectories appearing in a small number of time periods, resulting in a large amount of wasted space. To address this, the original encoding rules were improved to allow locations with non-overlapping trajectories from the same parent location to share the same vertical position (y-axis) interval.

[0049] As shown in Figure 5, a trajectory visualization method based on a timeline with hierarchical location information embedded in the present invention was used to generate a visual trajectory diagram containing 8 cases. Points A and B in the figure respectively show the possible contact of two cases in a village and the possible contact of three cases in a hotel.

[0050] The above examples demonstrate that the disclosed trajectory visualization method based on a timeline with hierarchical location information embedded can accurately and effectively present information in hierarchically structured descriptive trajectory data and help users understand the co-occurrence of these trajectories at locations of different granularities. By combining visual encoding methods, layout optimization algorithms, and encoding improvements, it can produce visualizations with high spatial utilization and good readability, and can objectively and effectively reveal trajectory characteristics, laying a solid foundation for the development of spatial information visualization.

[0051] The method described in the present invention is not limited to the embodiments described in the specific implementation manner. Those skilled in the art may derive other implementation manners based on the technical solution of the present invention, which also fall within the scope of the technical innovation of the present invention.

Claims

1. A trajectory visualization method based on embedding hierarchical location information into a timeline, the method comprising the following steps: S1. Extract the hierarchical descriptive trajectory data from the text description to be analyzed. For the two information elements of time and space, encode them using two position dimensions, horizontal and vertical respectively. Each trajectory will be mapped to a curve that changes in space along time. S2. Set an energy function for the optimization objective of the curve, and use a layout optimization algorithm to optimize the curve layout. S3. Allow the trajectories under the same parent location to share the vertical position interval without time interference at the locations, so as to improve the encoding rule, and then obtain the visualized trajectory embedded in the timeline.

2. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 1, wherein, The encoding using two position dimensions, horizontal and vertical respectively, in step S1 includes the following sub-steps: S11. Encode the time information with the horizontal position, and the horizontal position is linearly mapped from left to right to the time range involved in the hierarchical descriptive trajectory data. S12. Encode the location information with the vertical position. The locations in the hierarchical descriptive trajectory data are organized into a location hierarchy tree according to the administrative regions, and each location is mapped to an interval or a node on the vertical position.

3. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 1, characterized in that: The hierarchical relationship between locations in step S1 is reflected by the relationship of the corresponding intervals.

4. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 3, characterized in that: If location A contains location B, the interval on the vertical position corresponding to location A also contains the interval corresponding to location B. If there is no inclusion relationship between two locations, their corresponding intervals do not overlap either.

5. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 1, characterized in that: The optimization objective in step S2 includes at least one of minimizing the length of the trajectory curve, minimizing the number of intersections of the trajectory curve, and minimizing the total visualization height.

6. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 5, wherein, In step S2, the final energy function used in simulated annealing is the weighted sum of the energy functions corresponding to the optimization objectives: minimize E(x) = β1E length + β2E crossing + β3E white-space , where E length is the energy function corresponding to minimizing the trajectory curve length; E crossing is the energy function corresponding to minimizing the number of intersections of the trajectory curve; E white-space is the energy function corresponding to minimizing the total visualization height.

7. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 6, wherein, The energy function corresponding to minimizing the trajectory curve length is calculated by the following function:

8. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 6, wherein, The energy function corresponding to minimizing the number of trajectory curve intersections is calculated by the following function: E crossing =#(trajectory crossing).

9. The trajectory visualization method based on embedding hierarchical location information into a timeline according to claim 6, wherein, The energy function corresponding to minimizing the total visualization height is calculated by the following function: E white-space = Y max - Y min .

Images