Purdue-Zhang-MC3

VAST Challenge 2014
Mini-Challenge 3

 

 

Team Members:

Jiawei Zhang, Purdue University, zhan1486@purdue.edu     PRIMARY
Shehzad Afzal, Purdue University, safzal@purdue.edu
Dallas Breunig, Purdue University, dbreunig@purdue.edu
Jing Xia, State Key Lab of CAD&CG, Zhejiang University, xiajing@zjucadcg.cn
Jieqiong Zhao, Purdue University, jieqiongzhao@purdue.edu
Isaac Sheeley, Purdue University, isheeley@purdue.edu
Joseph Christopher, Purdue University, christ33@purdue.edu
David Ebert, Purdue University [Faculty Advisor], ebertd@ecn.purdue.edu
Chen Guo, Purdue University, guo171@purdue.edu
Shang Xu, Purdue University, xu537@purdue.edu
Jun Yu, Purdue University, yujun_2005@hotmail.com
Qiaoying Wang, Purdue University, wang1925@purdue.edu
Chen Wang, Harbin Institute of Technology, chwang@hit.edu.cn
Zhenyu Qian, Purdue University [Faculty Advisor], qianz@purdue.edu
Yingjie Chen, Purdue University [Faculty Advisor], victorchen@purdue.edu


Student Team:  YES

 

Team Number: 54

 

Streaming User ID: zhan1486@purdue.edu

 

Analytic Tools Used:

Our work utilized and extended SMART system, done by Purdue University VACCINE Center for social media visual analytics.

 

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

180

 

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

 

 

Video:

http://pixel.ecn.purdue.edu:8080/~zhan1486/Purdue-Zhang-MC3/Purdue-Zhang-MC3.wmv

 

Purdue-Zhang-MC3

 

 

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Questions

 

Please note - this challenge contains a question that is time-dependent.  Within 3 hours of starting the final data stream, send an email to VASTChal2014MC3@vacommunity.org containing your answer to question MC3.1.  Please include a copy of your answer to MC3.1 in your final answer form also. Your answers to MC3.2 and MC3.3, along with your video, are due July 8.

 

The responses to these questions should be incrementally built, as you (the contestant) acquire information from each streaming data segment you receive.  Your submission will answer these questions in consideration of all of the streaming data segments.

 

 

MC3.1 - Within 3 hours after start the final data stream, send an email to VASTChal2014MC3@vacommunity.org containing:

a.       An image showing the streaming data in your visual analytics tool. In this image, identify an event of interest that you intend to investigate further.

b.      The content of the final message in the data stream

 

The image showing the streaming data in the visual analytics tool:


Fig 1: screenshot of SMART system showing real time analysis of streaming data. In this image, the user is able to identify fire and standoff, the two major events during the time period from 20:00 to 20:30.


The content of the final message in the data stream:

  Time: 2014-01-23 21:32:00
  User name: gardener4958
  Message: RT @KronosStar There has been an explosion from inside the apartment building. Several people are down. #KronosStar #DancingDolphinFire #AFDHeroes

 

MC 3.2 - Describe the timeline of up to five major events that you discover in the streaming data. This timeline should include information from all three segments of the data stream if needed.    Use specific microblog records and call center data to support your description, but do not simply mimic back the data stream.  Provide a concise description of important participants, locations and durations.  Focus your response on the events themselves, rather than on the individuals reporting the events. Please limit your answer to no more than ten images and 1500 words.

 

We discovered 4 major events in the 3 data segments: POK rally, fire & explosion, hit & run, and shooting & standoff.

 


Fig 2: POK rally held in Abila City Park. Part of Parla St was closed due to concerns of public safety. Several key roles appeared in the rally including Sylvia Marek and Lucio Jakab. Some news media like abilapost actively posted updates of the rally. The system consists of multiple linked views including a) time series view; b) topic view; c) retweet/reply networks view; d) call center data table; e) microblog data table and f) map view. In time series view, blue bar charts represent microblog data, and red line charts represent call center data. In map view, red dots denote call center data and blue dots denote microblog data.

 

 


Fig 3: Fire event occurred at Dancing Dolphin Apartment at around 18:41.

 


Fig 4: A firefighter was injured in the fire event. Multiple linked views reveal the fact accordingly.

 


Fig 5: An unexpected explosion occurred at Dancing dolphin Apartment, at around 21:30.

 

 


Fig 6: A black van hit a car and a bicyclist, and escaped. Based on geo-located call center data and microblog data, the system helps investigate the possible trajectory of the suspect.

 

 


Fig 7: The black van chased by police was blocked at Gelato Galore and then opened fire.

 


Fig 8: The evolution of the shooting & standoff events.

 

 

 

 

MC 3.3 – Select one of your five major events from question MC 3.2 that you consider to be most likely to provide additional clues to the investigation of the GASTech disappearances.   Describe the roles of the participants.  Describe how other events you identified in MC3.2 may have influenced your selected event. Provide a hypothesis and evidence as to whom you suspect as being directly involved in the GAStech disappearances, either as perpetrators or victims.  Please limit your response to no more than five images and 500 words.

 

According to the timeline described in MC3.2, the shooting & standoff events provide clues to the GASTech disappearances. Based on monitoring the microblog and call center data traffic we think the disappearances of GASTech employees are due to the following two reasons:


However, all the above assumptions should be doubted due to the subjectivity of microblog users and lack of validation from multiple data sources.

In addition, all major events happening during the data stream are most likely related. Here is some evidence: (Fig.9)


Based on the evidence above, it is possible the POK members kidnapped two employees of GASTech and stayed somewhere near the Dancing Dolphin Apartments. Due to the fact that the hit & run was close to the Dancing Dolphin Apartments, it can be assumed the hit & run happened very soon after the kidnappers left their hideout. However, because the hit & run happened a significant period of time after the beginning of the fire, the kidnappers most likely were hiding in a location near the Dancing Dolphin Apartments and were “flushed” out of their hiding spot either by the crowd and heightened activity or by police officers ordering them to evacuate. The kidnappers most likely hit the car and the bicyclist by accident while trying to flee. It can be assumed the perpetrators of the hit & run and the shooting & standoff crisis are the same. It is possible the suspects travelled the route near the site of the POK rally because there were large crowds, which they thought may help them escape the pursuit of police officers.

In addition, the explosion following the fire event at Dancing Dolphin Apartments seems suspicious. The explosion occurred less than 10 minutes after the fire was under control. Some people were injured by the explosion. It can be possible that the explosion was caused by a gas leak, or by a deliberate attack. For a deliberate attack, it is possible that the fire event was also a man-made terror. Furthermore, the terrorists may try to murder victims by a fire event, which was later controlled by the firefighters. Finally they carried out the bombing attack.


Fig 9: Analysis of correlations among uncovered major events based on the timeline and spatial distribution of call center records.

 


Fig 10: Retweet/reply networks formed by users who mentioned GASTech related topics. Influential microblog users (having high degree centrality in the networks) like officia1abilapost and friendsofkronos provide clues to help explain the disappearance of GASTech employees. However, the statements should be doubted due to the subjectivity of microblog users and lack of validation from multiple data sources.