VAST Challenge 2014: Mini-Challenge 2

Qiao Cheng, Zhejiang University, chengqiaosdu@gmail.com
QiChao Wang, Zhejiang University, wqcriver@gmail.com
Yubo Tao, Zhejiang University, taoyubo@cad.zju.edu.cn,
Hai Lin, Zhejiang University, lin@cad.zju.edu.cn

The primary contact:    Qiao Cheng, chengqiaosdu@gmail.com

This is not a student team.

The analytic tool used for this challenge is developed by the submitting team. It was developed from May 2014

We give the permission of our submission to be posted in the publicly-accessible Visual Analytics Benchmark Repository.


video

Answers to the challenge questions:

MC2.1 – Describe common daily routines for GAStech employees. What does a day in the life of a typical GAStech employee look like? Please limit your response to no more than five images and 300 words.

Users can observe the trajectory patterns by interactively choosing the modes of working days, weekends and all the days, or specifying the different time intervals in some day. The observed trajectories can be from all the cars or some specific employee. We cluster all the trajectory destinations and encode them by blue dots. The size of dots is proportional to the number of trajectories. This image shows the trajectories of all the cars during the period between 6AM and 8AM in all working days. Besides staying in homes and the company, employees usually appear in the restaurant or cafeteria. It indicates that they often eat breakfast or buy the food on the way to the company.

This image illustrates the trajectories of all the cars during the period between 4PM and 6PM in all working days. Instead of stopping by the restaurants for breakfast, employees usually drive back home directly. The destinations of their trajectories are indicated by the name of the employee.

This image specifies the trajectories of all the cars during the period between 6PM and 11PM in all working days. It indicates that they rarely have activities during the nights in all working days.

This image shows the trajectory activity of employee Birgitta Frente in a working day. He often drives to shop in Hallowed Grounds on the way to company, and chooses the near way back home when getting off work. Similar conditions happened for other employees.



This image shows the trajectories of all the cars in weekends. Employees usually decide to have a long-distance travel or take a picnic. Also, these activities happen during the afternoon period.

MC2.2– Identify up to twelve unusual events or patterns that you see in the data. If you identify more than twelve patterns during your analysis, focus your answer on the patterns you consider to be most important for further investigation to help find the missing staff members. For each pattern or event you identify, describe

1. What is the pattern or event you observe?
2. Who is involved?
3. What locations are involved?
4. When does the pattern or event take place?
5. Why is this pattern or event significant?
6. What is your level of confidence about this pattern or event? Why?

Please limit your answer to no more than twelve images and 1500 words.

Most of Employees usually eat lunch in the company. But we find that some employees may go out for the hotel or restaurant during the lunch time. As shown in the image, these green trajectories mean that they drive to these different places along the roads for three or four times, not frequently. The red points mean that they consume in these places.

During the lunch time in the working days, some employees drive a long distance to some places without consumer records. Strange things may happen. For example, employee Hennie Osvaldo arrived to this place at 11:30 and went back at 12:24, Jan 9, 2014, staying for about an hour. Inga Ferro arrived to this place at 11:19 and left at 12:04, Jan 17, 2014. The time he arrived to this place is at 11:26 and left at 12:59, Jan 18, 2014.

By filtering the trajectory records during the early morning (2AM to 4AM), we do find some trajectories during this special time. The names of Employees are: Ingrid Barranco, Ada Campo-Corrente, Willen Vasco-Pairs, Orhan Strum, Sven Elecha. Isia Vann and Hennie Osvaldo arrived to place Isia Vann's home at 03:30, Jan 11, 2014. Hennie Osvaldo and Minke Mies arrived to place Sven Flecha's home at 03:40, Jan 9, 2014.

We find that destinations of some trajectories are located in Lars Azada’s home. By observing the time of these trajectories, they all happened at the night, Jan 10, 2014. The involved employees are: Vira Frente, Felix Balas, Axel Calzas, Isande Borrasca, Marin Orida, Isak Baza, Lucas Alcazar ,Nils Calixto and so on. We guess that a party or some kind of meeting happened. For example, Isande Borrasca arrived to his house at 19:12 and left at 22:29, Jan 10, 2014. Gustav Cazar arrived to his house at 18:45 and left at 23:23, Jan 10, 2014.

In the late night, we find that Lucas Alcazar drive back home at 01:10, Jan 7, 2014 and at 00:09, Jan 16, 2014.

Isia Vann usually drive to the company and back home through the same roads, as shown the red lines in the following image. But we find that at 23 o'clock, Jan 6, 2014, he drove to the Ada Campo- Corrente's home and stayed for one night, then drove to the company directly in the next day.

MC2.3– Like most datasets, the data you were provided is imperfect, with possible issues such as missing data, conflicting data, data of varying resolutions, outliers, or other kinds of confusing data. Considering MC2 data is primarily spatiotemporal, describe how you identified and addressed the uncertainties and conflicts inherent in this data to reach your conclusions in questions MC2.1 and MC2.2. Please limit your response to no more than five images and 300 words.

In the original data, we find that some trajectories consist of only one GPS point information. And we cannot find any GPS information around the current time for one specific car. This may be due to the error of GPS. So we ignore these GPS data, as shown the six blue points in the following image.
In the following image, we find that some trajectories have disorganized displacements deviating from the normal roads, and this influences our judgment. By observing the trajectories from different cars, we find that these GPS trajectories belongs to the car of Elsa Orilla. Then, we clear this abnormal data.