"CQUPT-Wei-MC2"
VAST
Challenge 2016
Mini-Challenge 2
Team Members:
Wei
Xueshi,Chongqing University of Posts and Telecommunications, xs.wei@foxmail.com PRIMARY
Zhang Zhihui, Chongqing University of Posts and Telecommunications, 1228791375@qq.com
Qin
Hongxing, Chongqing University of Posts and Telecommunications, qinhx@cqupt.edu.cn
Student Team: NO
Tools Used:
d3.js(A
JavaScript library for manipulating documents based on data)
Python(For
data preprocessing)
JavaScript(Programming
language)
Approximately how
many hours were spent working on this submission in total?
100 hours per person
May we post your
submission in the Visual Analytics Benchmark Repository after VAST Challenge
2016 is complete? YES
Video
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.
1. Figure 1.1 shows the scatter plot of prox
card data.The vertical axis is timeline, and the horizontal axis represents
prox zones. In terms of Figure 1.1, we can find the typical patterns visible as
follows:
1)Work days are from Monday to Friday.
2)Reasonable Work time is from 7:00 to 24:00.
3)There is one staff on duty at the weekend by turns.
Figure 1.1
2. From analysis of the track of employees,
it can be found that most employees usually move about on fixed floors. For
example, as shown in Figure 1.2, this employee's track never appears on the 3rd
floor; instead, it mostly appears on the 2nd floor. In Figure 1.3, the
employee's track mostly appears on the 3rd floor. Thus, besides stairs as well
as exits and entrances of the building, each employee usually moves about on
the floor where his or her office is located.
Figure 1.2
Figure 1.3
3. Figure 1.4 shows the track of every
employee in term of prox cards. We use different color points to represent
zones at different floors. The red, green, blue represent the first, second and
third floor respectively. The size of point represents the flow volume at the
zone. The gray line represents the trajectory at the same floor. The trajectory
between different floors is represented as the line with color, which is the
same with the color of the exit floor. We can find that a typical day for a
GAStech employees looks like:
1)Every employee must enter the building through a special zone
f1z1 on the first floor, in which every prox card appears firstly every day.
2)Employees mainly work on the same floor. It is deduced the fact
that the number of track lines between the same floors is much more than that
between different floors. Employees can reach other floors by stairs or
elevator zones.
Figure 1.4
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.
1.Figure 2.1 show the plot of Hazium sensor data
against time. The shapes of plots at four zones ( f1z8a, f2z2, f2z4, f3z1) are
almost the same. It means that air can flow freely from one zone to another
among these four zones.
Figure 2.1
2. Figure 2.2 shows the plot of Water heater Gas
Rate data and Water Heater Tank Temperature data. The status of two sensors is
stable at working time. The value of Water Heater Tank Temperature shows a
slight ascent stage.
Figure 2.2
3. Figure 2.3 shows the plot of data from all
lights power sensors on three floors. It reflects periodicity of lights power
during working time and off working time. During working time, many light
sources are on. Off working time, almost all light sources are off.
Figure 2.3
4. Figure 2.4 shows the plot of data from RETURN
OUTLET CO2 Concentration sensor. It can approximately reflect the number of
employees at the time. With the increasing number of employees, the density of
CO2 in the zone will rise. Otherwise, the density of CO2 will reduce.
Figure 2.4
5.Figure 2.5 shows the plot of data from all
Equipment power sensors on three floors. It reflects periodicity of equipment
power during working time and off working time. Power consumption during
working time is much more than that of time off work.
Figure 2.5
6. Figure 2.6 shows the plot of data from Drybulb
temperature sensor. The plot reflects a steady change of the data. It can be
induced that the outdoor temperature changes smoothly during two weeks. No
danger can be caused by the factor of temperature.
Figure 2.6
7. Figure 2.7 shows the distribution of the
number of different departments of the company. It shows that there are more
employees at engineering department than other departments, and there are the
least employees in HR department.
Figure 2.7
8. Configures in the building were maintained by
a special worker every working day. In terms of Figure 2.8, the employee with
ID "earpa001" appears in the zone "f1z1" and
"f1z6". The zone "f1z2" is the entrance of the building.
The zone "f1z6" is the Conf zone.
Figure 2.8
9. From Figure 2.9, it can be found that the
change of the density of hazium has an effect on HVAC Electric Demand Power,
and that HVAC system operates spasmodically. Especially in the early morning
from June 11th to June 12th , it has the most obvious effect on the operation
of HVAC system when there appears an abnormal condition to the density of
hazium.
Figure 2.9
10. Figure 2.10 shows the scatter plot of prox
card data. We found that no employee had entered into two zones, which are the
zones f3z5 and f2z5. In terms of the maps of the prox zones, we learned that
the zone f3z5 is for Future Expansion. We deduce that the zone f2z5 may be a
special zone, which employees have no access to, or it may be a non-office
area.
Figure 2.10
MC2.3Describe
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.
1. AT 1 o'clock on Jun. 1st, two employees, whose
ID numbers are "ibaza001" and "edavies001", directly went
to the zone f1z1 from the zone f2z1 without passing through the elevator zone.
It can be found in Figure 3.1.
Figure 3.1
2. During the period from 12 o'clock on June 7th
to 11 o'clock on June 8th, the value of "SUPPLY INLET Temperature" is
much higher than other time. I believe that an unusual event happened. The
event can be caused by the unusual event on HVAS system.
Figure 3.2
3. Only on the day of May 31st, dose a pretty
high value, gotten from the F_1_Z_2:Equipment Power sensor, appeared during the
two weeks. Therefore, we believe that the equipment power sensor report an
unusual event.
Figure 3.3
4. In terms of Figure 3.4, the value of Total
Electric Demand Power at 7 o'clock on Jun. 7th and 8th is much higher than that
at other time. We believe that an unusual event may happen during the two days.
Figure 3.4
5. Figure 3.5 shows the plot of
"Availability Manager Night Cycle Control Status". The plot of the
sensor data on floor3 is different from that of the sensor data on floor1 and
floor2. The plot of the sensor data on floor3 shows that on/off status of HVAC
system was always off. It means that the "Availability Manager"
system may be out of order or be turned off by someone.
Figure 3.5
6. By comparing the plot of "SYS COOLOING
COIL Power" on different floors. We found that the plot on the third floor
did not accord with that on the first and secnd floor, especially on June 7th
and June 8th. We believe that it may be an unusual event.
Figure 3.6
7. Figure 3.7 shows the plot of "SUPPLY FAN: Fan Power". During the first weekend, the pattern of "SUPPY FAN: Fan Power" of floor3 is not line with that of floor1 and floor2. The Fan Power on Floor3 ran at a higher level, when the Fan Power in Floor1 and Floor2 ran at a lower level. We believe that an unusual event on floor3 led to the change of Fan Power.
Figure 3.7
8. In terms of Figure 3.8, the density of Hazium in four zones, which are "f1z8a", "f2z2", "f2z4" and "f3z1", rose quickly and got the highest value on day of 11th, June after the employees whose Id numbers are "mbramar001" and "ostrum001", left the zones. The day was Saturday. On the first Saturday, no employee entered into any zones. Thus, we believe that it is an unusual event, which may be made by the employees.
Figure 3.8
9. The variation of the value of "Thermostat Cooling Setpoint" sensor during the period from 7th June to 9th June is much more than that during other period. We believe that there is an unusual event about temperature in the building during June 7th to June 9th.
Figure 3.9
10. In terms of Figure 3.10, SYS COOLING COOL
Power on every floor maintained a much higher level from seven o'clock on June
11st to seven o'clock on June 12nd than other time. During seven o'clock on
June 11st to seven o'clock on June 12nd, the density of hasium rose much higher
than other time. Therefore, we believe that there is an unusual event of SYS
COOLOING COIL Power. The unusual event may be caused by the unusual event of
hasium density.
Figure 3.10
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.
1.In terms of Figure 4.1, we found that no employee passed through the zone "f3z5". By checking the maps of the prox zones, it is verified that the zone "f3z5" is a "Future Expamsion" zone.
Figure 4.1
2. In terms of Figure 4.2, we found that most trajectories between different layers passed through the fourth zone on every floor. By checking the maps of the prox zones, the fourth zone on every floor is the elevator or stairs. It is the only channel by which employees can go to other floors.
Figure 4.2
3. In terms of Figure 4.3, we found that no employee had entered into the zone "f2z5". We also found no special information about the zone by checking the maps of the prox zones. Thus, we believe that the zone isn't an office zone, or an employee without privilege cannot enter into the zone.
Figure 4.3
4. In terms of Figure 4.4, we found that within two special employees can enter into the zone "f1z6". By checking the maps of the prox zones, it is shown that the zone is for configure. We believe that the two special employees are the configure maintainer, who entered into the zone to maintain some configure.
Figure 4.4
5. In terms of Figure 4.5, we found that the flow volume of the zone "f1z1" is the largest. We believe that this zone is the only entrance and exit to the building.
Figure 4.5