Entry Name: "USTUTT-Herr-MC2"

VAST Challenge 2014
Mini-Challenge 2

Team Members:

Dominik Herr, University of Stuttgart, dominik.herr@uni-stuttgart.de PRIMARY

Robert Krueger, University of Stuttgart, robert.krueger@vis.uni-stuttgart.de

Florian Haag, University of Stuttgart, florian.haag@uni-stuttgart.de

Thomas Ertl, University of Stuttgart, thomas.ertl@uni-stuttgart.de

Student Team: NO

Analytic Tools Used:

Only own developments Examples:

Approximately how many hours were spent working on this submission in total?

400

May we post your submission in the Visual Analytics Benchmark Repository after VAST Challenge 2014 is complete? YES

Video:

https://www.youtube.com/watch?v=LIVTn5DFFtU&feature=youtu.be
(alternativ mirror, same video) https://cloud.visus.uni-stuttgart.de/public.php?service=files&t=30b91379e472d94bf9201c00d869c8c4&download

VAST-MC2

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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.

During the week GAStech employees drive to work around 7 am, while most of them get a coffee on their way. Overall, GAStech employees always drink coffee and there are lots of coffee-places around. The employees live in the north-west to south-east area, near the parks.

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Working hours are usually from 7:45 am to 12:10 pm and from 1:45 pm to 6:00 pm. During lunch (areas in between) the employees usually go to Guy's Gyros, Katherina's Café, Chostos Hotel and others. When they go for lunch/dinner often one employee pays for a round of ~5 people. This often leads to bills up to $100. The daily routines can be seen in the sequenceview in the following figure (marker 1). The colors of the areas of interest (AOI) in the geoview map (top) correspond to colors in the sequenceview (bottom). Some of the employees in the IT and security sector do not leave GAStech for lunchbreak, possibly due to work overload (2). After work the employees drive home for a short break before meeting again in the south-west area of Abila, at Katherina's Café, Guy's Gyros or at the Ouzeri,... (1). Some also do private business (e.g. shopping). These evening meet-ups are also celebrated at the weekend when fewer go for lunch (3). On Sunday most of the executive officers play golf at Desafio Golf Course (4).

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The truck drivers have other daily routines. However, they often share stays at GAStech and join their collegues for lunch and dinner. The truck drivers daily jobs bring them to the airport, the harbor, the hospital, factories and the refinery with transactions of more than $1000. While three of them seem to have Friday off (parked at GAStech, 6), two also work Fridays. Overnight, trucks are parked at GAStech and at the hospital. The following image shows car trajectories in red and trucks in blue.

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Using our pattern filter we can filter for the common daily pattern (Coffee->GAStech->Lunch->GAStech->Home).

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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

a. What is the pattern or event you observe?

b. Who is involved?

c. What locations are involved?

d. When does the pattern or event take place?

e. Why is this pattern or event significant?

f. 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.

1) While all employees with rented cars have GPS logs from Januar 6th, Sten Sanjorge Jr., the president of GAStech, first appears on Friday, Jan 17, at 7:56am for the first time at Hallowed Grounds. At that time he pays $9.72 and his car sends GPS information for the first time. At 1:28pm he pays $15.95 at Abila Zacharo, likely for his lunch. At about 8 o’clock he uses his loyalty card at Katharina’s Café. On the next day he pays $600 at the Chostus Hotel, where he stays. The height is noteworthy, because the second highest bill at the hotel is just below $200. On Sunday, the last recorded day, he plays Golf with the other executive officers of GAStech at the Desafio Golf Course where he pays $148.22 at 3:46pm. In the evening he joins other employees for dinner at Guy’s gyros. The following image shows our Semantic Movement Explorer where we filted for all persons who share stays (same AOI during same time). The filter pattern is shown on top (1). The bottom shows the filtered sequences. On Friday, January 19 one can see the green bars, indicating they meet at the golf course. This can be further explored using a tooltip and using the geoview. Using a geographic lense tool we filter for all trips which end at the golf course. The picture shows the home locations of the other executive, living near Spetson Park (3).

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2) On some days (Jan 7,8,9,11,13, and 14) there are shifts in the night watch (mostly at 3 am) of the security employees. Obviously they live in the south-east and drive to the rich area below Spetson Park. All executives, except the president, live there. On some days however these shifts do not happen. Security staff involved are: Loreto Bodrogi, Minke Mies, Hennie Osvaldo, and Isia Vann. The image below highlights two of these shifts in the sequence diagram and shows the movement directions. In the small box we filtered for executive homes. Here, Ada Campo-Conrrente appears in the tooltip.

3) The GPS positions of Elsa Orilla’s car are erroneous. A sample of the GPS data is shown in the screenshot below.There seem to be two problems with her GPS signal:
a. The signal has white static, which reduces the precision of her GPS signal a lot, but it is still possible to visually compensate for the static.
b. The signal seems to have a constant offset of her actual position. We come to this conclusion because her car always seems to be at the Ouzerie Elian during the business hours. Most of the other employee’s cars are located at GAStech during this time.

Please click on the images for full resolution.

4) Lucas Alcazar has a credit card transaction of $10,000 at Frydos Autosupply n’ More. The median of all transactions at Frydos is $146.74 and the standard deviation is $1053.96, so Alcazar’s purchase differs more than nine times the standard deviation from the median. We discovered this finding through the usage of our transaction explorer, which is depicted in the screenshot below. Also, he works at GAStech in between 10pm and 11pm. This happens four times during the two weeks of which data is given. One possible explanation could be that Alcazar is working at the IT help desk and needs to attend to a matter that can only be worked on during the night.

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5) Although Axel Calza’s home seems to be near Spetson Park, during the night of January 6 his car is located at Hippokampos and during January 12 his car is located at Albert’s Fine Clothing. In both cases he has a transaction at the respective locations. This observation is based on the location of his car and his transactions in the evening, but the question where he stayed during those nights remains.

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6) Axel Calzas also skips work during the morning of Thursday, January 9 and stays at the Ouzeri on January 14. This leads to the assumption, that Calzas might be rebellious – which is always suspicious (highlights in previous figure).

7) Truck driver Valeria Morlun buys goods at Katherina’s Café. Since the amount she paid is mediocre, about $20, this seems to be a private purchase. This is a finding, because the challenge description explicitly states that trucks must not be used for private purchases. This finding has been proven in the transaction explorer and the semantic movement explorer respectively. The following image shows the filter expression to proof if trucks drive from somewhere to restaurants (1) and the corresponding result sequences (2, restaurants in cyan). On the bottom of the image (3) the blue truck trajectory reveal that Kathrina's Café is one of the destinations.

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8) Brand Tempestad and Isande Borassca sometimes go to the Chostus Hotel for lunch (image below). This is noteworthy because Sten Sanjorge Jr. stays at this hotel once he arrives in Abila (see first answer in 2.2)).

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9) On Saturday, January 18, some of the employees are at Kronos Capitol in Abila Park. Three of them are securities. This might be due to an event that took place in that area.

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10) Often the person that pays for a purchase is not the same person that used his or her loyalty card for the transaction. Consequently the credit card user and the loyalty card user must know each other and they must be at the same place at the same time. Please click on the images for full resolution.

11) On some days, especially in the second week, some trucks have higher trip frequencies. They often only stop for a few seconds at a location. This could be due to the massive workload. While most trucks do not drive on Fridays one does.

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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.

To map, enrich and analyze the data we provide two highly interactive visual tools: ‘Semantic Movement Explorer’ and ‘Transaction Explorer’. At the beginning no semantic information about trips and transactions, as well as no direct mappings are given.

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In our approach the analyst is integrated in the mapping-process and our tools support the enrichment in an interactive visual way. We first start the process in our Semantic Movement Explorer, where the analyst can translate and scale the touristmap to align it to the road network (see figure below).

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Second, the analyst can annotate AOIs using a polygone draw tool and the tourist map information (see image below: 1, 2). Here AOI name and category can be defined. Doing so, all movements ending in that area are automatically enriched with this semantic destination information. Furthermore, we also provide an automated AOI extraction, which maps transaction location names to movement positions (3). The analyst can create, load, edit and delete annotation settings. The image below shows the annotation steps:

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Afterwards (4), the analyst can investigate all enriched movements and their stays at different locations in the sequenceview. The sequenceview for example is shown in the answer to the questions 2.1 and 2.2. Our pattern filter (see screenshots of previous questions) helps to create and proof hypotheses of common or abnormal behavior. To get a deeper understanding of employment structures and transactions we use our Transaction Explorer tool. It consists of a colored table/matrixview (marker 1) and various filter options (2). It also helps to get an overview of persons, employment types, and locations (3). Datacells are enhanced with scatterplots, showing the deviation of matching transactions (4).

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Here we can investigate all transactions and map loyalty and credit card transactions by timestamp, amount and location. Via a service layer between both tools (Semantic Movement Explorer and Transaction Explorer) share the enriched information.