Wolfgang Jentner, University of Konstanz, firstname.lastname@example.org PRIMARY
Juri Buchmueller, University of Konstanz, email@example.com
Hanna Schaefer, University of Konstanz, firstname.lastname@example.org
Thilo Spinner, University of Konstanz, email@example.com
Rita Sevastjanova, University of Konstanz, firstname.lastname@example.org
Fabian Sperrle, University of Konstanz, email@example.com
Dirk Streeb, University of Konstanz, firstname.lastname@example.org
Udo Schlegel, University of Konstanz, email@example.com
Student Team: NO
N.E.A.T. - Novel Emergency Analysis Tool, developed by the University of Konstanz
Demo available to try out at:
Approximately how many hours were spent working on this submission in total?
May we post your submission in the Visual Analytics Benchmark Repository after VAST Challenge 2019 is complete? YES
1. Generate a master timeline of events and trends during the emergency response. Indicate where it is uncertain and which data underlies that uncertainty. Limit your response to 1000 words and 12 images.
Our general approach to tackle such a timeline consists of the application of streamgraphs and episodes in the NEAT tool described in task 4. We identified interesting points in time according to the volume of Rumble messages per category and district. Also, we compared the statistical deviation of the measured radiation from normal levels using the streamgraphs and applied the episode plots to determine peoples reaction to the events. Basically, episode plots represent a significantly dense occurring set of n-grams (messages from Y*INT) that exceed a given threshold, thus probably being important (e.g. by amount of retweets).
However, besides the analysis with NEAT and the annotations created in situ, we also composed a timeline of events in a written format:
Time and Date
14:05 - 06.04.
Power Outage or Server Failure of the Rumble App - can be observed in the rumble app data volumes (MC1) - contrary official canal in messages show power outage but at 13:39 (MC3) - high time uncertainty between MC data
14:33 - 06.04.
Small earthquake hits the city - official message on message canal (MC3) - people talking about it in the messages, but it was only a small one without damage (MC3)
18:08 - 07.04.
Preperation course for disasters in Neighborhood 1 (MC3)
08:36 - 08.04.
Large Earthquake hits the city - large increase in rumble app traffic (MC1) - major sensor outages (MC2) - increase in messages (MC3)
09:11 - 08.04.
Fire at the nuclear plant (MC3) also fence down and broken main building (MC3)
09:15 - 08.04.
Bridges closed until inspection (MC3) - people spoke a few days before about bad inspectors for bridges - unsecure bridges
09:49 - 08.04.
Fire Alert to evacuate buildings (MC3) also real fire in a lot of areas (MC3)
10:00 - 08.04.
Brick buildings collapse (MC3) in old town - hit very hard by earthquake (MC1)
13:00 - 08.04.
Water contamination and sewer damage (MC3) - repair emergency teams are sent out to help (MC3)
13:39 - 08.04.
Power Outage (MC3) - not observable in the rumble app (MC1) or the sensors (MC2)
13:56 - 08.04.
Heavy Hospital Damage (MC3) - only one completely functional hospital (MC3)
14:30 - 08.04.
Famous Singer Missing (MC3) - after her apartment collapsed and could be under the damaged structures (MC3)
16:40 - 08.04.
Fake? Contamination - High values but if compared to location these are located in the wilson forest - either there is some contamination there or it is a sensor failure (MC2) - this is bad as ther is a large shelter in the wilson forest (MC3)
07:00 - 09.04.
Power Restored in some parts of the city (MC3)
09:01 - 09.04.
Sewer System damaged heavier than expected and repair takes longer than expected (MC3)
09:30 - 09.04.
Free Concert from some famous singers also missing singer included (MC3) - Infrastructure Damage
14:36 - 09.04.
Another mediocre earthquake (MC3) - can also be seen in the rumble app data (MC1)
15:28 - 09.04.
High school collapsed (MC3)
09:30 - 10.04.
Schools closed in Old Town, Scenic Vista, Broadview, Chapparal, Easton, and Oak Willow (MC3)
11:59 - 10.04.
Last shake kills messages (MC3) no further data and last time shake gets reported by rumble app (MC1)
Hospitals getting closed (MC3)
Fatalities rumors rise and rise to more than 500 but also go back to 50 (MC3)
Chemical conspiracy theories arise (MC3)
Rumble App gets used more often during earthquake events (MC1)
Contamination Sensors fail more and more during time (MC2)
People care for each ofter and give free food and space (MC3)
People start to feel more and more alone (MC3)
Libraries are new shelters (MC3)
People don't want to move to shelters because they already have a spot they like (MC3)
Dark jokes about having no tsunamis at least (MC3)
Some grocery stores only sell limited amount of supplies (MC3)
Mobs start rumbling around town (MC3)
Shelters get too crowded (MC3)
2. Identify and explain cases where data
from multiple mini-challenges help to resolve uncertainty, and identify cases
where data from multiple mini-challenges introduces more uncertainty. Present
up to 10 examples. If you find more examples, prioritize those examples that
you deem most relevant to emergency response. Limit your response to 1000 words
and 12 images.
We see an almost identical increase in dangerous radiation measures and radiation measures from Wilson Forest. This might indicate some external causality, faulty data, or even the source of the radiation.
We see a high entropy (one of our representative uncertainty measures) for Old Town Rumble reports during the whole phase between the major earthquake peak and a second measurement peak shortly afterwards in Rumble. This might indicate, that there is some problem with Rumble reports during this phase, especially in Old Town.
We see that during the increase in reporting of dangerous radiation, there is no change in radiation reporting in Safe Town. This could indicate that the area is already evacuated.
We see almost no reports of damages during the first smaller earthquake, but we see an increase in Y*INT reports. Additionally, our uncertainty measure (entropy) indicates a peak for the same datapoint in the Rumble dataset. This could demonstrate a high difference between reports.
During the second earthquake, the errors is the other way around. While Rumble clearly reports high shaking intensities, the Y*INT messages only have a slight peak. Later on, when the full consequences of the earthquake unfold, the Y*INT also shows a high peak.
In Rumble we see a third earthquake shortly after the second one. However, this does not reflect in the Y*INT dataset. Additionally, the sudden stop of Rumble reports after the second earthquake, might indicate, that there was some system downtime and previous reports were coming in later.
During day 9 we see continuous reports in Y*INT, but only two small spikes in Rumble and a slow decrease in the radiation volume. This creates a lot of uncertainty about whether another incident has happened. Or which of the three is true.
3. Are there instances where a pattern emerges in one set of data before it presents itself in another? Could one data stream be used to predict events in the others? Provide examples you identify. Limit your response to 500 words and 8 images.
4. The data for the individual mini-challenges can be analyzed either as a static collection or as a dynamic stream of data, as it would occur in a real emergency.. Were you able to bring analysis on multiple data streams together for the grand challenge using the same analytic environment? Describe how having the data together in one environment, or not, affected your analysis of the grand challenge. Limit your response to 500 words and 10 images.