Entry Name: UCD-Griffin-MC2

VAST Challenge 2014
Mini-Challenge 2

Team Members:

Kevin Griffin, University of California, Davis, kevgriffin@ucdavis.edu PRIMARY

Student Team: YES

Analytic Tools Used:

1. MC2GeoViz, developed by Kevin Griffin for ECS 289h Big Data Visualization class, taught Spring 2014 by Dr. Kwan-Liu Ma

2. Microsoft Excel

Approximately how many hours were spent working on this submission in total? 200 (I didn’t do a good job at tracking this)

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

Video:

http://www.kevinsgriffin.com/Kevin_Griffin/Demo_files/UCD-Griffin-Video.mov

UCD-Griffin-Video

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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.

A typical GAStech employee will start their morning by first stopping by a coffee shop and arriving to work at around 8:00am. They will usually have a 1.5 to 2-hour lunch break, which includes the time to travel to and from the restaurant and to eat. Lunchtime is usually between 12:00 and 14:00. The workday usually ends around 17:3 and dinner is consumed at a nearby restaurant.

Image 1: Lucas Alcazar typical workday

Image 2: Felix Balas Typical Workday

Image 3: Hideki Cocinaro Typical Workday

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. Unusual Event

- Lucas Alcazar returning to GASTech at approximately 22:14 on 1/6/2014. This is significant because it’s outside of normal work hours

- Confident about this event because there are multiple data points correlating this event

2. Unusual Event

- Lucas Alcazar left GAStech around 1:10 on 1/7/2014 and returned to work at his regular time (approximately 8:00) the same day. This is significant because it’s outside of a typical employee’s work hours.

- Confident about this event since there are numerous tracking data points that support this event

3. Unusual Event

- Loreto Bodrogi was out for approximately 4 hours (3:34 to 7:29) on 1/7/2014 before arriving at GAStech around 8:00. This is significant because of the early morning hours during the week (Tuesday).

- Confident since multiple tracking data points correlate to this event

4. Unusual Event

- Lucas Alcazar left Ouzeri Elian around 21:16 on 1/8/2014 and went to GAStech and stayed until approximately 23:53. This is significant because of the abnormal work hours.

- Confident of this event since multiple tracking data points correlate to this event

5. Unusual Event

- Loreto Bodrogi and Minke Mies are in the same location at approximately 3:30 on 1/9/2014. Minke appears to travel back home while Loreto is still in the area until around 7:24 when Loreto heads to GAStech.

- This is significant because of the early morning hour during the work week

- Confident since multiple tracking data points correlates to this event

6. Unusual Event

- Hennie Osvaldo arrives in the vicinity of (IVO) Spetson Park around 23:00 on 1/13/2014 and doesn’t leave until approx. 3:30 the next day. It also appears that Minke Mies meets up with Hennie at 3:30 and doesn’t leave until around 7:47.

- This is significant because of the early morning hour that these two are IVO Spetson Park on a Thursday.

- Very confident since multiple tracking data points and credit card purchasing correlate to this event

7. Unusual Event

- Lucas Alcazar returns to work at approximately 22:36 on 1/15/2014. This is significant because of the late hour to return work.

- Confident since multiple tracking data points correlate to this event

8. Unusual Event

- Lucas Alcazar left work on 1/16/14 after midnight. This is significant because it’s outside of normal work hours

- Confident since multiple tracking data points correlate to this event

9. Unusual Pattern

- Isande Borrasca and Brand Tempestad have credit card charges at the same hotel (Chostus Hotel), around the same time, and on the same dates (1/8/14, 1/10/14, 1/14/14, and 1/17/14)

- Very confident since multiple tracking data points, credit card, and loyalty card purchases correlate to this pattern

10. Unusual Event

- Lucas Alcazar returns to GAStech at 20:35 on 1/17/2014 after visiting Ouzeri Eilan and leaves at 22:41. This is significant because it’s outside of normal working hours.

- Very confident since multiple tracking data points, credit card, and loyalty card purchases correlate to this event

11. Unusual Pattern

- Felix Resumir made multiple visits to Frydos Autosupply n’ More around the same time each day and spending a significant amount of money. This is significant because of the number of visits to the same location and the amount of money spent during each visit.

- Very confident because multiple tracking data points, credit card purchases, and loyalty card data correlates to this pattern

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.

Conflicts, uncertainties, and missing data were addressed by using other available data to resolve any ambiguities. For example, if loyalty card data was only available about a purchase and I wanted to know the time that the purchase was made, which the loyalty card data doesn’t have, I would use the vehicle tracking data to find a data point as close as possible to the purchase location. Some missing data, like business locations, was iteratively added through user interactions with the credit card and loyalty card markers (see image 1 and 2 below). Once the location of the businesses was disambiguated through the use of tracking data and/or the tourist map, the user can drag these markers to the correct location and their positions will be remember for subsequent analysis. Time and location conflicts were resolved on a “majority rules” basis. If there were more supporting data in the area supporting a different time then that time would be used. Image 3 shows a good example when this “majority rules” concept would be used. The data suggests that a credit card purchase was made at 12:00. However, the surrounding tracking data (Image 4) suggests that this purchase was done at around 7:15. The latter time will be used since more data (actually the rest of the data) supports the credit card transaction happening around 7:15.

Image 1: Credit Card marker with wrong business location

Image 2: Credit Card marker with the correct business location

Image 3: Wrong Credit Card transaction time

Image 4: Tracking data with correct time