Entry Name: VRVIS-Matkovic-MC2
VAST Challenge 2016
Mini-Challenge 2
Team Members:
Rainer Splechtna, VRVis Research Center, Vienna, Austria,
Splechtna@VRVis.at
Michael Beham, VRVis Research Center, Vienna, Austria, Beham@VRVis.at
Denis Gracanin, Virginia Tech, Blacksburg, VA, USA, gracanin@vt.edu
Alexandra Diehl, University of Buenos Aires, Buenos Aires, Argentina,
adiehl@dc.uba.ar
Claudio Delrieux, CONICET and U.N.S., Bahia Blanca, Argentina,
cad@uns.edu.ar
Kresimir Matkovic, VRVis Research Center, Vienna, Austria,
Matkovic@VRVis.at
Student Team: NO
Tools Used:
ComVis, a
visual analytics research tool developed at VRVis Research Center
Numerous
Python scripts for data processing
Approximately how many
hours were spent working on this submission in total?
100
May we post your
submission in the Visual Analytics Benchmark Repository after VAST Challenge
2016 is complete? YES
Video
https://drive.google.com/open?id=0B1K_OL3S7HTCZmEtd3QxMTN6a0E
Questions
MC2.1 What are the
typical patterns visible in the prox card data? What does a typical day look
like for GAStech employees?
Limit your response to no more than 6 images and 500 words.
Working hours: There are three main
types of working hours for GAStech employees. A scatterplot in Figure 1-1a shows
start of the working day and end of the working day in minutes. We can clearly
see employees who come early and go before others come. Then, employees who
come in the morning and go in the afternoon, and, finally, the employees who
come around 4pm and stay until midnight. If we link the scatterplot with a
histogram showing departments, we can see some interesting patterns. The
histogram shows the distribution of employees according to department (there is
one record per employee for each day she was in the office. For most employees
there are ten records, there are some with nine and some with eleven records).
The engineering department has the most employees, followed by the facilities
and the IT departments. The HR is the smallest department. The Administration,
Executive, and HR departments work always during the regular working hours. The
IT and Engineering departments work from 8am to midnight, and the facilities
department is the only department active during all three working time periods.
Interestingly, security is in the building only during the regular working
hours, from 8am to 5:30pm. Figure 1-1 shows the start and the end times for
each department.
Figure 1-1
Mobile proximity sensors: The scatterplot in Figure
1-2a shows counts from fixed proximity sensors and mobile proximity sensors. We
see that there are employees who were never tracked by mobile robot. If we
brush them, we see that they belong to the Administration, Facilities,
Engineering and IT departments (Figure 1-2b). The employees who were tracked by
mobile robot either receive a post, or they cross path with the robot. A simple
brush of employees with 52 (the most) fixed logs includes two employees (three
records). The record with the most mobile logs (40) belongs to Herroro Kannon
from Security. The other two records belong to Patrick Young from Facilities.
All three records belong to different days. Figure 1-2c shows a table view used
to show details on demand.
Figure 1-2
Departments and working hours: If we plot working time
in hours versus department we can see some interesting patterns as well.
Besides one clear outlier who stayed for 17 hours in the building, the
employees who sometimes spent just few hours belong to the Executive and
Administration departments. In other departments there are clear clusters, but
times are similar (Figure 1-3a). Figure 1-3b shows an interesting pattern when
hours in building and EndTime are depicted.
Figure 1-3
Occupancy: Up till now we dealt with
records that represent one employee per day. Such data organization supports an
employee-centric analysis. As we have spatial data we also computed
zone-centric data. For each zone we have occupancy curve which tells us how
many employees were in a zone in 1 minute or 15 minute intervals of a day. If
we show all occupancy curves, we can see the main patterns. There is a high
occupancy during the day, and not so much is going on during the night. There
is more activity before midnight in the night, then after. However, we do not
know to which zones the curves belong to (Figure 1-4a). We introduce a new
view, Occupancy Map, where we depict occupancy curves per zone as overlay on
the floor plan. Figure 1-5 shows the curves for 3 floors. As expected, the
entrance area and the elevator are the busiest zones on the first floor. The
deli activity is also expected, there is clear peak in the middle of the day,
and a smaller peak in the evening. Interestingly, the large meeting room (zone
5) is rarely used. Most of the zones have quite similar patterns over 14 days
(there are 14 curves in each curve view in the map). The zones on the third
floor exhibit more variations and this could be worth a further investigation.
Figure 1-4
Figure 1-5
Logging frequency: We also provide a map
view with trajectories which facilitates deeper analysis. A screenshot of
several linked views from an analysis session is shown in Figure 1-6. The aforementioned
map view is in the top right corner. The circles represent frequency of fixed
proximity sensors logs for different zones.
Figure 1-6
MC2.2 Describe up to
ten of the most interesting patterns you observe in the building data. Describe
what is notable about the pattern and explain what you can about the
significance of the pattern.
Limit your response to no more than 10 images and 1000 words.
Supply side inlet
temperature: on weekends is higher (highest on Sundays) than on weekdays and lower
during office hours (Figure 2-1).
ญญญ
Figure 2-1
Data fluctuation: Fluctuation of Supply side outlet temperature, Water heater
tank temperature, Water Heater Gas Rate (Figure 2-2) is lower on weekends
(left) than on weekdays (right).
Figure 2-2
HVAC power demand: HVAC power demand is higher
during weekends than on weekdays and exhibits a regular pattern over the course
of a day, i.e., oscillates between a minimum and maximum value (Figure 2-3). On
the second weekend (purple) a more pronounced oscillation is noticeable than on
the first weekend (orange). If we take a look at the thermostat heating and
cooling setpoints in the zones we can see that on floor 1 (Figure 2-4 upper
left) and floor 2 (Figure 2-4 upper right) the heating setpoint was higher and
the cooling setpoint (shown for floor 2 in Figure 2-4 lower right) lower on the
second weekend (purple) than on the first weekend (orange). Hence the increased
HVAC power demand. The settings on floor 3 (Figure 2-4 lower left) did not vary
on the two weekends.
Figure 2-3
Figure 2-4
On workdays during the night a similar pattern on a lower level is noticeable.
On weekdays during office hours the demand rises and stays on a certain level
(week one is shown in Figure 2-5). In week two an anomaly occurred, i.e., a
spike in the power demand (see answer MC2.3) but the general pattern remains
the same. Light power is constant in some areas, likely due to cleaning and
safety.
Figure 2-5
Light
Power and Equipment Power: If the light is switched on, after several seconds
the equipment power is turned on. Two days are brushed Graphic: 31.5. (violet)
and 1.6. (yellow) on floor 3, Zone 7
(Figure 2-6).
Figure 2-6
In the zone view we can see that the lights on
floor 1 in zone 3 (entrance) and the zones 8A and 8B are always on (shown in
Figure 2-7 top, all days are brushed). The lights in the corridors on floors 2
and 3 are also always turned on. On weekends the lights in the offices on floor
2 are turned off (Figure 2-7 middle). During weekday office hours the lights in
the offices are turned on and off several times per day (Figure 2-7 bottom).
Figure 2-7
CO2 level: During weekdays the CO2
concentration rises over the course of a day and drops during the night (Figure
2-8 top). During weekends the CO2 concentration is rather low (Figure 2-8
bottom).
Figure 2-8
Server room: (Floor 3 - Zone 9) The parallel coordinates view
shows the relationship among different sensed variables in the server room.
Brushed in red (Figure 2-9) are the CO2 values higher than 1000 ppm (considered
as poor air by international standards). Top right scatterplot view shows the
CO2 concentration at the server room during the two analyzed weeks, with peaks
at Tuesday 7th and Wednesday 8th. Bottom
scatterplots show the same information for the third floor corridors.
Considering that there is a low entrance to the server room, the CO2
concentration can be used as a good indicator of the air quality in the entire
building. Notably, we observed that the central corridor shows a lower minimum
CO2 concentration.
Figure 2-9
Water heater tank temperature and gas rate: Furthermore, we found a
relation between Water Heater Tank Temperature and Water Heater Gas
Temperature. The orange line brush (Figure 2-10) shows the temperature on 4 June,
the green on 5 June, the pink brush on 10 June
and the blue line on 11 June.
Figure 2-10
Another interesting fact
is that on Saturdays (orange and pink brush) the temperature is heated and
cooled more than on Sundays (blue and green brush).
MC2.3 Describe up to
ten notable anomalies or unusual events you see in the data. Describe when and
where the event or anomaly occurs and describe why it is notable. If you have
more than ten anomalies to report, prioritize those anomalies that are most
likely to represent a danger or serious issue for building operation.
Limit your response to no
more than 10 images and 1000 words.
Short working day: When
we show the time of each employee in the building for all days, we see an
outlier. On 9 June, Bramar Mat is only 47 minutes in the building and 41.5
minutes in the office (Figure 3-1).
Figure 3-1
Number of working days:
(Figure 3-2)
●
Most of employees worked ten days, from Monday to Friday both weeks.
●
Four employees worked during weekend (orange bars: Lagos Linda, Bramar Mat, Carrara Lise, Sturm Orhan), three of them (Lagos Linda, Bramar Mat, Carrara Lise) worked a total of 11 days during two weeks.
●
Two employees worked nine days (Lea Sherell, Barranco Ingrid).
Figure 3-2
Change in trajectories:
Executive Edward Earlean also have an unusual behavior. From Tuesday to Friday,
he always goes to his office and to the conference room in the first floor. On Monday,
Edward does not go to the conference room but stays in zone 2 on floor 3 (Figures
3-3b and 3-3c). Furthermore, a mobile sensor has tracked Edward Earlean on
Monday, 13 June. The trajectories
on Mondays look different.
Figure 3-3
Sensor readings: Sensors behave strange from 2016-06-07
at 7:00 am to 2016-06-09 at 5:00 am (approximately):
●
variable F_#_Z_#RETURN OUTLET CO2 Concentration behave very strange,
going very low to very high twice.
-
2016-06-07 at
7:00 am - Lowest
-
2016-06-07 at
22:00 pm - Highest
-
2016-06-08 at
7:00 am - Lowest
-
2016-06-08 at
22:00 pm - Highest
-
All the other days, it was medium to low (below 900). Values higher than
1000 ppm are considered high for working environment.
https://www.kane.co.uk/knowledge-centre/what-are-safe-levels-of-co-and-co2-in-rooms
Figure 3-4
CO2 Concentration gets
lower when REHEAT COIL Power is turned on.
Particularly strange days:
● Thursday, 9 June: high Hazium level
● Saturday, 11 June: high Hazium level
MC2.4 Describe up
to five observed relationships between the proximity card data and building
data elements. If you find a causal relationship (for example, a building event
or condition leading to personnel behavior changes or personnel activity
leading to building operations changes),
describe your discovered cause and effect, the evidence you found to
support it, and your level of confidence in your assessment of the
relationship.
Limit your response to no more than 10 images and 1000 words.
Hazium level and employee presence: 2 employees are at work on
Saturday, Bramer Mat (works on floor 3, zone 2) and Strum Orhan (works on floor
3, zone 8). Both went to their respective offices. When Bramer Mat goes home at
1am, the Hazium level increases significantly.
Figure 4-1
shows trajectories of Bramar Mat (orange) and Strum Orhan (violet).
Figure 4-1
Figures 4-2
(Bramar Mat) and 4-3 (Strum Orhan) show power consumption and Hazium levels:
● Top Left: Light power consumption
● Top Right: working on Saturday
● Bottom Left: Equipment power
consumption:
○ Start Point: increase in Light power
consumption (Top Left)
○ End point: decrease in Light power
consumption (Top Left)
● Bottom Right: Hazium level:
○ Start Point: increase in Light power
consumption (Top Left)
○ End point: decrease in Light power
consumption (Top Left)
Figure 4-2
Figure 4-3
CO2 level and employee presence:
Edward Earlean is out of office between 11:30am and 1pm. He goes to the conference
room on the first floor. When he leaves the room at 11:30am, then the Return
Outlet C02 Concentration decreases. When he comes back at 1:30pm, the C02
concentration increases (violet curve), as shown in Figure 4-4.
Figure 4-4
The
corresponding raw data (records) are listed below:
2016-06-03 09:40:00, 3, 580.000000, 15, 21,
3, mobile-prox, 3
2016-06-03
11:29:11, 3, 689.183333, 64, 56, 1,
fixed-prox, 3
2016-06-03 11:29:47,
3, 689.783333, 79, 56, 4, fixed-prox, 3
2016-06-03
11:30:28, 3, 690.466667, 79, 56, 4,
fixed-prox, 2
2016-06-03
11:30:59, 3, 690.983333, 79, 56, 4,
fixed-prox, 1
2016-06-03
11:31:01, 3, 691.016667, 103, 56, 1,
fixed-prox, 1
2016-06-03
11:31:29, 3, 691.483333, 97, 33, 6,
fixed-prox, 1
2016-06-03
13:06:01, 3, 786.016667, 103, 56, 1,
fixed-prox, 1
2016-06-03
13:06:57, 3, 786.950000, 79, 56, 4,
fixed-prox, 1
2016-06-03
13:07:38, 3, 787.633333, 79, 56, 4,
fixed-prox, 2
2016-06-03
13:08:21, 3, 788.350000, 79, 56, 4,
fixed-prox, 3
2016-06-03
13:08:23, 3, 788.383333, 64, 56, 1,
fixed-prox, 3
2016-06-03 13:08:36, 3, 788.600000, 6, 11, 3, fixed-prox, 3
2016-06-03 14:36:00, 3, 876.000000, 15, 21, 3, mobile-prox, 3
...