Analysis of Evacuation TracesW. Freiler, K. Matkovic, Z. Konyha∗

VRVis Research CenterVienna, Austria

D. Gracanin†

Virginia TechBlacksburg, VA, USA

T. Lipic, R. Miklin, and M. Beric ‡

Department of Telecommunications — FERUniversity of Zagreb, Croatia

ABSTRACT

We describe the tools and techniques used in our analysis of theVAST 2008 Challenge regarding the evacuation traces based onthe data from RFID tags. We applied visual analytics to exploreand describe the evacuation caused by an explosive device. Thatincluded identifying casualties, witnesses and suspects. We usedComVis tool and its multiple linked view capabilities, includingPathView visualization, to show positions and paths of all personsin one view. ComVis facilitated better collaboration among teammembers due to its ability to capture the context and state of visualanalysis (views, brushes) for a given data set.

Keywords: Visual analytics, multiple linked views, PathView.

Index Terms: I.3.0 [Computer Graphics]: General—; I.3.6 [Com-puter Graphics]: Methodology and Techniques—(Interaction tech-niques);

1 INTRODUCTION

We describe analysis of the traces dataset (VAST 2008 mini chal-lenge) that consists of positions of people in a Miami, Florida De-partment of Health (DOH) Building, just before and shortly after anexplosion. All people were tracked, and for each person, a positionis provided for each time step as two dimensional coordinates. Intotal, there are 836 time steps and 82 people in the dataset. A map ofthe building in a bitmap format is also provided. We analyzed thedataset using a coordinated multiple view tool and identified sus-pects and injured persons. We also detected some interesting factsabout the evacuation and spotted a problematic area in the building.

2 DATA AND TOOLS

We mapped the given data to a data model which contains familiesof mappings as described in [1]. For each person we computed thepath as a series of cells over time. Furthermore we computed thecumulative path length for each person as a function of time andspeed in each time step as a function of time as well. This gave usa data table with 82 rows (one for each person) and four columns:Person ID, path, cumulative path length, and single time step path.The first column (Person ID) contains scalar values while the otherthree columns contain time series. In order to analyze such a datasetwe used ComVis, a coordinated multiple view system developed atVRVis. ComVis supports conventional views for scalar values, anda few special views for families of function graphs. We used pathview and curve view extensively. All views are linked and the usercan brush in one view and corresponding items are highlighted in allother views. Besides single brushing, ComVis supports compositebrushes using boolean operation in an iterative manner. Brushing inthe curve view is realized by a simple line which selects all curvesthat cross it.

∗e-mail: freiler@vrvis.at, matkovic@vrvis.at, konyha@vrvis.at†e-mail: gracanin@vt.edu‡e-mail: tomislav.lipic@fer.hr, ranko.miklin@fer.hr, mario.beric@fer.hr

PathView supports a background map and arbitrary paths of var-ious lengths. In order to analyze our data set we extended PathViewslightly. We added an animated mode so we can watch the persons’movements. We display the path as a poly-line with a small circleat the end. There is an option to display just a limited number ofrecent steps. In this way a trailing line is drawn behind the spot.The length of the trailing line indicates the speed — the longer thetrailing line is, the faster a person is walking. This is visible onstatic images as well. Figure 1 (left view) shows a PathView show-ing the complete paths. Only the last ten steps are shown in themiddle view and the curve view (right view) depicts the cumulativepath lengths over time.

ComVis is (still) a single-user application but it can capture thestate of the visual analysis session (brushes, views, etc.) and store it(together with the data being analyzed) in a single file (.cvv file).Such files can be exchanged among collaborators to provide thecommon context and framework for visual analysis. That made itpossible for our team members located in Vienna (Austria), Zagreb(Croatia), and Blacksburg (USA) to better leverage regular commu-nication channels (audio/video conferences, workshops) using thecommon visual analysis context captured in a .cvv files.

3 INTERACTIVE VISUAL ANALYSIS

We analyzed the data using ComVis. The animation feature of thepath view has proven to be extremely helpful. Cumulative pathlengths depicted in the curve view show some interesting patterns(Figure 2). We can clearly see some curves which are increasingin length from the early time steps. These curves represent personsmoving around at this time. Persons staying in the same cell havethe zero value for this period of time. Interestingly, some of the per-sons which were walking in the beginning continued to move to thevery end (constantly increasing path). Two persons were moving inthe beginning and stopped after some time. A large group of peo-ple started to move around time step 375 indicating a sudden eventwhich caused almost everyone to move. The bomb went off at thismoment and an alarm was issued. It triggered the movement. Somepeople managed to find the exit faster, and some continued walkingto the end. There was also a small group of people that started tomove and then stopped, and the persons with IDs 18 and 36 whohardly moved at all. Simple brushing in the curve view allows us tosee corresponding paths. We can also brush the paths in a PathViewand see the corresponding curves in the curve view.

3.1 Suspects and Casualties

We brushed interesting groups of curves as described above anddeveloped some hypotheses. Although the person moving the most(ID 29) exhibits an unusual behavior, we do not think that personis a suspect but rather a medical employee working in the buildingthat just happened to walk around. We then selected a group ofpeople that moved very little. Some of them did not move at all,and some stopped the movement shortly after the explosion. Weused an animated PathView to further examine this cases.

It seems the device was set off at position (66,32) because manypeople near this spot stopped moving shortly after the incident. Weassume that these people are dead or at least unconscious. Shortlybefore the incident, a person walked into this room, waited from

Figure 1: A combination of PathView and curve views. The first PathView (left view) shows the complete paths, the second PathView (middleview) shows the last ten steps of the paths while the curve view (right view) shows cumulative path lengths over time

Figure 2: Pathview (upper view) shows traces near the explosivedevice, including the current position — timestep 487 (red dots) andthe last ten steps (trailing lines). Curve view (bottom view) shows atime-series visualization of the path lengths of all people. Horizontallines represent persons that do not move. The traces with very shortpath lengths, all of them are near the bomb site, indicate casualties.

time steps 243 to 280, and walked out again. The location wherethis person paused could be the bomb location. Furthermore, thisperson also did not leave the building immediately, but waited forperson 1 to leave first, because being the first one outside may besuspicious. Unless this was a suicide attack, we think that this per-son (ID 21) had placed the bomb. Interestingly, our suspect walkedmuch slower than all others and walked a very straight path.

By broadening the brush a little bit, we included some additionalpeople with short paths, leading us to further conclusions. Mostcasualties occurred near the explosion, and only our suspect wasable to escape the building. The person with ID 36 moved duringonly one step, from time step 243 to 244. This person either diedor lost RFID Tag. The person with ID 59 returned to the building,probably to recover something important. From the time step 693this person does not move any further (probably died).

3.2 Evacuation

We examined overall evacuation as well (Figure 3). The evacuationwas quite orderly, with the exception of the lower left corner. Thepeople there were not really endangered by the explosion, but thisarea was very crowded, and some part of the hallway were probably

Figure 3: Situation during the evacuation (timestep 588). The sus-pect (orange, middle right) has already escaped. The traces with nomovements near the explosive device indicate casualties. Some peo-ple (light green, top right) managed to escape, but from one momenton, people (red, center top right) turn around, and stop moving soon(due to fire, debris) and since they do not move we can assume thatthey died. People far away from the explosive device survived but thehalf of the hallway seems to be blocked (dark green, center bottomleft). Some people even turned around and tried to find another wayout (purple, center top left).

blocked. Therefore, some people changed their mind and selectedanother route on the far left; some people even changed their mindtwice. This is also the area where two people that were already nearthe exit turned around and went back.

Another interesting behavior can be observed in the top rightcorner. We suppose there was a group of seriously injured peopleor some unusual behavior was present. Persons with ID 3, 20, 37,and 65 were in the same room. Two of them started to move a littlebit earlier. Interestingly, one of them, ID 65, stopped moving anddid not manage to leave the building. That person was close to thedoor and then returned. Similar behavior at the same spot (comingclose to the door, going back, and then stopping) was observed forseveral more people (IDs 23, 65, 78, and 69). Since some peopleleft the building there and others turned back and run in the oppositedirection, we suspect fire or a collapsed wall blocked the exit.

4 CONCLUSION

We organized the data in such a way to allow the use of multiple co-ordinated views, the PathView and curve view. The combination ofthe PathView and curve view proved to be the key technique to suc-cessfully analyze the data. Brushing in the curve view (cumulativepath lengths) clearly identified different categories of traces.

REFERENCES

[1] Z. Konyha, K. Matkovic, D. Gracanin, M. Jelovic, and H. Hauser. Inter-active visual analysis of families of function graphs. IEEE Transactionon Visualization and Computer Graphics, 12(6):1373–1385, 2006.