ISSS608 2016-17 T3 Assign ERIC PRABOWO CUNDOMANIK

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Background

Data

Solutions and Answers

Comments and Feedbacks

 


Comments and Feedback [in progress..]

1

Feedback from Josef Exconde

For the first graph presented, though it is a neat idea to use color to show whether the reading is High or Low, I find that there is some problem to this approach, 1.) We don't have a baseline unto which is really a high reading and which is low, we don't know the acceptable value of each reading and if we assume that 0 is the acceptable value then everything should be high, so even if it is a nice representation we can't seem to make the most out of it for this viz, and secondly having 9 lines in a facet or a pane is difficult by itself, having 9 lines with the same color scheme it makes it harder to differentiate one sensor over the other. If I want to single out a reading for each sensor I would need to manipulate the filter. Personally I would suggest either having a label for each line or using color to differentiate them.

Another is the Sensor Reading Pattern, though you stated a great finding regarding the lack of reading for Appluimonia when there is a spike from AGOC-3A, the packed information on one screen makes it hard to observe the finding. The value on the columns and rows are too small to comprehend and the dimensions won't properly fit in one screen altogether.

2


Hi Eric,

Well done on your work.

Few of my comments that you can look into are as follows:

  • instead of using bar graph you can may be use line chart for question 3
  • For question 2 you can make the days as your x axis and chemicals as your y axis

Thanks & Regards

Debasish Behera

3



Hi Eric

Thank you for considering my critics valuable :D

Here are a few points from my end:

  1. Question 1 - Sensor Analysis Chart/Dashboard on Tableau Public can be improved by finding a way to segregate the sensors (aside from the filter mechanism). Besides, the Avg line should have been imposed on the same graph rather than using a new facet/row for showing the average reading reference.
  2. Question 1 - Low/High segregation as pointed out by Joseph may not be the best approach for finding the faulty sensors.
  3. Question 2 - A line chart may not be the best way of pointing out the patterns/differences in the chemicals. Perhaps a calendar or heatmap visualization could be better in communicating the differences in the chemical's pattern.
  4. Question 3 - The dashboard for question 3 looks fairly nice and tidy

Overall, the analysis looks good barring few visualizations that may seem ambiguous to the end user on a first glance. However, if we can explain those visualizations precisely, then it will further strengthen the analysis and report for Mitch and other environmentalists.

Once again, thank you for giving me the opportunity to provide feedback.

Cheers

Asmit



Tableau Dashboard Overview

Link: https://public.tableau.com/profile/eric.prabowo#!/vizhome/MC2-EricPrabowo-v2_0/STORYLekagulWildlifePreserveArea


Question Answers

Question 1

Characterize the sensors’ performance and operation. Are they all working properly at all times? Can you detect any unexpected behaviours of the sensors through analysing the readings they capture? Limit your response to no more than 9 images and 1000 words.

Sensor 4

Sensor performance and operation are overall good, except for sensor 4 which shows an unusual pattern compared to sensor 3 and 5, which is nearby. Sensor 4 have a problem on detecting the amount of chemical pollution, where it is increasing in August and December compared to April. Probably the measurement sensor is not performing well from the unusual behaviour.

Q1-eric prabowo.png
Sensor 4 unusual behaviour of capturing data (increasing pattern)

Sensor 3

Sensor 3 shows unusual measurement of chemical pollution detected. Overall average of measurement is higher for sensor 3 within 3 months of data, for all chemicals detected. This indicates measurement problem of the sensor 3, where measurement unit calculation is inaccurate. Conversion of the data should provide a more accurate result.

Q1 1-eric prabowo.png
Sensor 3 unusual behaviour of measurement (shown as outlier)

In details, the data also given a prove that wind direction does not match the amount of chemicals detected on the sensors as shown in the image below. As a result, the amount of Appluimonia and Chlorodinine is probably not the real detected amount of chemicals.

Q1 2-eric prabowo.png
Dashboard showing wind direction and Sensor 3 readings unrelated

Sensor 9

Sensor 9 shows unusual measurement of chemical starting 23rd of August / last quarter of August, and the whole December. This pattern might show a broken sensor indication from Sensor 9 for certain chemicals reading.

Q2 3-eric prabowo.png
Sensor 9 have got a different measurement on Appluimonia, Chlorodinine, and Methylosmolene starting on the last quarter of August and the whole December.
Sensor 7

The dashboard data also shows that sensor 7 is not performing well, as Chlorodinine is showing up on the sensor, while the wind direction is not showing indication of direction from any factories.

Q1 3-eric prabowo.png
Dashboard showing wind direction and Sensor 7 readings unrelated

Sensor Reading Pattern

When readings on AGOC-3A is intense, sensors cannot read both AGOC-3A and Appluimonia at the same time altogether. So, when it comes to high intensity reading of AGOC-3A, Appluimonia data is not actually recorded by the sensors, as shown in image below. There is also an indication of blank data every second (2nd), fourth (4th), sixth (6th), seventh (7th) day of the month at exactly 12am, indicating sensor maintenance or restart, where it is not capturing data.

Q2 5-eric prabowo.png
Sensor Reading Counts – Sensors cannot read both AGOC-3A and Appluimonia at the same time [updated]

Question 2

Which chemicals are being detected by the sensor group? What patterns of chemical releases do you see, as being reported in the data?

Chemical Hourly Release Pattern

On hourly filtered data, the chemicals detected have patterns of releases. These indicates production hour of certain chemicals detected.

Q2-eric prabowo.png
Hourly filtered sensor data – showing chemical release patterns by hour of the day[updated]

As shown on the line chart above, the pattern shows production time that uses AGOC-3A is 05:00-20:00 in August. Production pattern is shown by aggregated daily data within 3 months on the similar hour of the day. This production pattern time gap is increasing in August and December. In August, production time is 05:00-23:00. In December, AGOC-3A production time is 06:00-23:00, however there is an additional peak of AGOC-3A waste at 04:00. Second pattern shown on the data is that, Methylosmolene is being released by production starting from 9pm to 6am in the morning. This shows production patterns of Methylosmolene is done in midnight hours.


Chemical Significant Readings
Q2 2-eric prabowo.png
Chemical significant readings – Appluimonia

On the Dashboard Monitor, Appluimonia shows significant read from most of all sensors. This shows indication that the chemical pollution is not coming from the factories. Instead, it might come from surrounding areas.


Chemical Monthly Release Pattern

Chemicals reading pattern for AGOC-3A shows there are monthly indication of lower production at the very beginning of the month, and no production at the end of the month. This chemical pattern shows production behaviour of companies using AGOC-3A, where production tend to increase in the middle of the month, and stopped at about day 25th of every month. Except for December, production stops early at 22nd December.

Q2 4-eric prabowo.png
Production pattern of AGOC-3A

Question 3

Which factories are responsible for which chemical releases? Carefully describe how you determined this using all the data you have available. For the factories you identified, describe any observed patterns of operation revealed in the data.

Companies responsible for the chemical wastes are:

  • Kasios: AGOC-3A
  • Radiance ColourTek: AGOC-3A, Chlorodinine
  • Roadrunner Fitness Elect: Methylosmolene, Chlorodinine, AGOC-3A
  • Indigo: Methylosmolene, Appluimonia

Kasios
Q3-eric prabowo.png
Sensor 5 showing indication of wind direction from Kasios Office – AGOC-3A detected at 12PM

Radiance ColourTek
Q3 1-eric prabowo.png
Sensor 9 showing indication of wind direction from Radiance ColourTek – AGOC-3A detected at 12PM
Q3 5-eric prabowo.png
Sensor 8 and wind direction indicates Radiance - Chlorodinine detected at 12PM

Roadrunner Fitness Elect.
Q3 2-eric prabowo.png
Sensor 6 showing indication of wind direction from Roadrunner Fitness Elect. – Methylosmolene detected at 12AM
Q3 3-eric prabowo.png
Sensor 6 showing indication of wind direction from Roadrunner Fitness Elect. – Chlorodinine detected at 3AM
Q3 6-eric prabowo.png
Sensor 5 detected AGOC-3A coming from the wind direction of Roadrunner

Indigo Sol Boards
Q3 4-eric prabowo.png
Sensor 9 and wind direction indicates Indigo - Methylosmolene detected at 3AM
Q3 7-eric prabowo.png
Sensor 9 detected Appluimonia coming from the wind direction of Indigo Sol Boards at 12 PM