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Embedded map many-to-many mobile data visualization way Maptrix is useful, many novices are not very clear about this, in order to help you solve this problem, the following editor will explain in detail for you, people with this need can come to learn, I hope you can get something.

How to visualize the flow information in geographical location is a widely studied problem, especially the many-to-many flow information, such as the movement of animals, the spread of diseases, goods or knowledge. The following work proposes a more effective visualization form Maptrix to help users analyze this type of data, and designs a reasonable and detailed user study to compare the effectiveness of the proposed maptrix visualization form with Bundled Flow Map and OD Matrix.

The visualization effects of the three visualization methods Maptrix,Bundled Flow Map and OD Matrix are shown in figure 1.

Figure 1 comparison of visualization effects of Bundled Flow Map,OD Map,Maptrix

The research on the problem of mobile data in maps has a long history. This problem was applied to the visualization of railway map for the first time, but the visualization method at that time had the phenomenon of overlap and intersection of visual elements. at present, there are three kinds of solutions to this problem. By converting visual elements, that is, connecting edges into density maps, but using density maps will lose a lot of detailed information in the map. The second type is through edge binding, but edge binding is more suitable for one-to-many scenarios; the third solution to this problem is through interaction and aggregation.

The second way to visualize the flowing data in geographical location is through OD matrix visualization, but the OD matrix loses the location information in the map, so there are some studies that can add the missing geographic location information through small multiple.

However, through the analysis of the above related work, we find that there is little work to combine the map with the OD matrix to solve the problem of many-to-many visualization in the map, and the visualization method of MapTrix is proposed.

There are three main components of Maptrix visualization, namely, the map representing the starting place of the mobile data, the map representing the ending place of the mobile data, and the OD matrix. In order to facilitate the user to find its internal pattern, the OD matrix is rotated to ensure that the horizontal axis and vertical axis of the OD matrix are symmetrically distributed. To establish the relationship between the matrix and the map, it is necessary to connect the OD matrix with the starting map and terminate the map. In order to ensure that the connection lines between the location on the map and the matrix do not cross, and to ensure a better visual effect, the work of this paper uses quadratic programming to find the location of the connection point, as shown in figure 2 below are the three main constraints of the connection line.

Fig. 2 three types of constraints

The purpose of the three constraints from left to right is to prevent the crossing between the connecting line and the point connecting point, to prevent the crossing between the connecting lines in the opposite direction, and to prevent the crossing between the connecting lines in the same direction.

In order to verify the effectiveness of the proposed visualization forms, this paper designs effective and comprehensive user study for the three visualization forms of Maptrix and Bundled Flow Map,OD Map. The task in the user study designed in this paper has been designed in detail, and the design task is often used in the map. According to the scope of recognition, it is mainly divided into three types: identification in the whole map; identification in the range of a single location; identification in the range of some areas. The specific abbreviations, descriptions and examples are shown in Table 1 below.

Table 1 Task Design for user study

The information obtained from the analysis of user study is mainly divided into two points:

The RF type (regional flow, which analyzes flow data within a region) takes the longest of the three types of tasks listed.

Compared with the other two kinds of visualization methods, Bundled Flow Map and OD Map,Maptrix are similar in efficiency with Bundled Flow Map.

In view of the above two findings of * user study, this paper then designs a second user study for RF-type tasks and the effectiveness of the visualization methods of Maptrix and Bundled Flow Map.

First of all, the tasks of RF type are divided in detail, and the efficiency of judging what type of task is low. At the same time, for the two kinds of visualization forms of Maptrix,Bundled Flow Map, data sets of different complexity are used to verify their effectiveness.

The specific criteria for dividing tasks of RF type (regional flow, analyzing flow data within a certain area) are mainly the following two aspects

According to the characteristics of the global data flow, the dominant data flow that needs to be identified is within the AB region or between the An and B regions.

For the adjacency of location elements, there are three specific situations:

The specific location selected is within the region, and the regions are adjacent to each other.

The specific location selected is within the region, and the selected locations within the region are adjacent to each other.

There is no adjacency between the selected location and the area.

Through the results of the second user study, we find that under different data complexity, the expression ability between OD map and matrix is still very similar; the time consumed by regional flow type data is still very high, but it can help users to understand through interaction, such as when the user clicks on the selected area during the execution of the task.

However, in view of the relationship between the complexity of the dataset and the time it takes to implement the task, with the continuous increase in the complexity of the dataset for which the task is executed, the efficiency of executing the task does not increase accordingly. for example, the complexity of data flow between provinces (states) in China is lower than that in the United States, but the speed of performing tasks in the United States is better than that in China, which may be because users are more familiar with maps of the United States than those of China. At the same time, the map of the United States is more regular than that of China, and it will also be of great help to perform tasks, so the complexity of data is not directly related to the performance of tasks by users.

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