IIS428 2017-18 T1 Assign Felicia Anto Christy Charles Jawahar

Overview

Mistford is a mid-size city is located to the southwest of a large nature preserve. The city has a small industrial area with four light-manufacturing endeavors. Mitch Vogel is a post-doc student studying ornithology at Mistford College and has been discovering signs that the number of nesting pairs of the Rose-Crested Blue Pipit, a popular local bird due to its attractive plumage and pleasant songs, is decreasing! The decrease is sufficiently significant that the Pangera Ornithology Conservation Society is sponsoring Mitch to undertake additional studies to identify the possible reasons. Mitch is gaining access to several datasets that may help him in his work, and he has asked you (and your colleagues) as experts in visual analytics to help him analyze these datasets.

Mitch Vogel was immediately suspicious of the noxious gases just pouring out of the smokestacks from the four manufacturing factories south of the nature preserve. He was almost certain that all of these companies are contributing to the downfall of the poor Rose-crested Blue Pipit bird. But when he talked to company representatives and workers, they all seem to be nice people and actually pretty respectful of the environment.

In fact, Mitch was surprised to learn that the factories had recently taken steps to make their processes more environmentally friendly, even though it raised their cost of production. Mitch discovered that the state government has been monitoring the gaseous effluents from the factories through a set of sensors, distributed around the factories, and set between the smokestacks, the city of Mistford and the nature preserve. The state has given Mitch access to their air sampler data, meteorological data, and locations map. Mitch is very good in Excel, but he knows that there are better tools for data discovery, and he knows that you are very clever at visual analytics and would be able to help perform an analysis.

Background

Manufacturing Companies near Mistford

Roadrunner Fitness Electronics

Roadrunner produces personal fitness trackers, heart rate monitors, headlamps, GPS watches, and other sport-related consumer electronics. Roadrunner began as one of the region’s first fitness stores in 1962, with an eye toward outfitting the entire nation with appropriate outdoor gear. After an earthquake nearly destroyed their main warehouse in 1968, Roadrunner turned a bad situation into a glowing success with the first “slightly damaged goods” sale. After which they began to focus on manufacturing; though their “Earthshaking Bargains” business still sells dented, overstocked and refurbished items over the internet and from a small retail shop attached to their front office.

Kasios Office Furniture

Kasios Office Furniture manufactures metal and composite-wood office furniture including desks, tables, and chairs. Kasios wants to do with desk chairs what Starbucks did for coffee – making office furniture what people must have, instead of what they just need. “Office equipment doesn’t need to be ugly!” says founder Ken Kasios. “We have redesigned all office products to be cool, fun, and hip—even your basic stapler.” Kasios business model is focused on in-store merchandising highlighting the beauty and functionality of their “user-centered design”. They recently celebrated the one-year anniversary of a distribution and merchandising agreement with the national office supply chain store PaperKlips.

Radiance ColourTek

Radiance produces solvent based optically variable metallic flake paints. “Metallic paints with an untarnished reputation!” quips ColourTek’s Senior Vice President Arthur Donner. “Radiance ColourTek metallic paints are worth their weight in gold.” Offering a new generation of paints in the 1970s, Radiance out marketed all competitors for three decades until manufacturing process issues began to tarnish their reputation. “We were challenged,” said Donner. “Polishing up our pearlescent pigments caused us to lose luster, but now we have the lowest VOCs (volatile organic compounds) in the industry!”

Indigo Sol Boards

Indigo Sol produces skateboards and snowboards. Founder Billy Keys started off manufacturing wooden wine barrels for northwestern US wineries, but then navigated a course from decorative fiberglass wine barrels to making his first pair of fiberglass skis in 1971. Excellent product and sales decisions rocketed Keys Skis production to unexpected levels, until they were bought out by a large Denver, Colorado-based private investment group. Keys returned to making specialized snowboards in the 1980s, with a small company in Mistford called Indigo Sol. The company has seen modest growth in recent years.

Chemicals

Appluimonia

An airborne odor is caused by a substance in the air that you can smell. Odors, or smells, can be either pleasant or unpleasant. In general, most substances that cause odors in the outdoor air are not at levels that can cause serious injury, long-term health effects, or death to humans or animals. However, odors may affect your quality of life and sense of well-being. Several odor-producing substances, including Appluimonia, are monitored under this program.

Chlorodinine

Corrosives are materials that can attack and chemically destroy exposed body tissues. Corrosives can also damage or even destroy metal. They begin to cause damage as soon as they touch the skin, eyes, respiratory tract, digestive tract, or the metal. They might be hazardous in other ways too, depending on the particular corrosive material. An example is the chemical Chlorodinine. It has been used as a disinfectant and sterilizing agent as well as other uses. It is harmful if inhaled or swallowed.

Methylosmolene

This is a trade name for a family of volatile organic solvents. After the publication of several studies documenting the toxic side effects of Methylosmolene in vertebrates, the chemical was strictly regulated in the manufacturing sector. Liquid forms of Methylosmolene are required by law to be chemically neutralized before disposal.

AGOC-3A

New environmental regulations, and consumer demand, have led to the development of low-VOC and zero-VOC solvents. Most manufacturers now use one or more low-VOC substances and Mistford’s plants have wholeheartedly signed on. These new solvents, including AGOC-3A, are less harmful to human and environmental health.

Task

General Task

The four factories in the industrial area are subjected to higher-than-usual environmental assessment, due to their proximity to both the city and the preserve. Gaseous effluent data from several sampling stations has been collected over several months, along with meteorological data (wind speed and direction), that could help Mitch understand what impact these factories may be having on the Rose-Crested Blue Pipit. These factories are supposed to be quite compliant with recent years’ environmental regulations, but Mitch has his doubts that the actual data has been closely reviewed. Could visual analytics help him understand the real situation?

The primary job for Mitch is to determine which (if any) of the factories may be contributing to the problems of the Rose-crested Blue Pipit. Often, air sampling analysis deals with a single chemical being emitted by a single factory. In this case, though, there are four factories, potentially each emitting four chemicals, being monitored by nine different sensors. Further, some chemicals being emitted are more hazardous than others. Your task, as supported by visual analytics that you apply, is to detangle the data to help Mitch determine where problems may be. Use visual analytics to analyze the available data and develop responses to the questions below.

Specific Task

Problem 1: About Sensors

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

To access the performance and operation of the sensors, the total readings of all the sensors are plotted for each day, and each hour, filtered by the month, using a line graph. From this graph, it is possible to conclude the month, day and hours at which all the sensors did not pick up a reading, suggesting malfunction of the sensors.

Figure 1.1

For the month of April, no readings were recorded on Day 2 and Day 4 at Hour 0 (Figure 1.1).

Figure 1.2

For the month of August, no readings were recorded on Day 4 and Day 7 at Hour 0 (Figure 1.2).

Figure 1.3

For the month of December, no readings were recorded on Day 2 at Hour 0 (Figure 1.3).

Hence, it is concluded that on Day 2 and Day 6 of April, Day 4 and Day 7 of August and Day 2 of December, none of the sensors were working at Hour 0. However, there may still be other instances whereby some of the sensors might have worked while others did not, which would not be reflected by the above line graph. To find these instances, the readings are plotted for each day, and each hour, filtered by the month and the sensor, using a heatmap.

Figure 1.4

There are 2 such instances. The first is found in the month of August on Day 2, whereby Sensor 7 did not record a reading at Hour 0 (Figure 1.4). Similarly, Sensors 1, 2, 4, 5, 6, 8 and 9 did not record readings for Hour 0 on Day 2 of August.

Figure 1.5

The other instance is on the month of December on Day 7, whereby Sensor 3 did not record a reading at Hour 0 (Figure 1.5). Similarly, Sensors 1, 2, 4, 5 and 9 did not record a reading at Hour 0 on Day 7 of December.

To conclude, it is found that on Day 4 of August, only Sensor 3 was working at Hour 0 whereas Sensors 1, 2, 4, 5, 6, 7, 8 and 9 were not working. Similarly, on Day 7 of December, only Sensors 6, 7 and 8 were working at Hour 0 whereas Sensors 1, 2, 3, 4, 5 and 9 were not working.

Figure 1.6

For all the months, and all the sensors, the total readings are highest on Day 2 at Hour 4 at 318.5 ppm (Figure 1.6). Filtering by month, the sensor that contributes greatest to this high reading can be found.

Figure 1.7

Sensor 6 is the greatest contributor for the readings on Day 2 at Hour 4, at 195.8 ppm (Figure 1.7). Even though there at 9 sensors, Sensor 6 alone has contributed to more than half of the readings recorded on Day 2 at Hour 4. By filtering the months, the month(s) in which this behavior happened can be found.

Figure 1.8

Figure 1.9

The readings for Sensor 6 are exceptionally high on Day 2 at Hour 4 for the months April (Figure 1.8) and December (Figure 1.9).

Problem 2: About Chemicals

Now turn your attention to the chemicals themselves. Which chemicals are being detected by the sensor group? What patterns of chemical releases do you see, as being reported in the data? Limit your response to no more than 6 images and 500 words.

All four chemicals – AGOC-3A, Methylosmolene, Chlorodinine and Appluimonia are detected by all nine sensors across all three months. By plotting the chemical readings on each sensor, filtered by the month, the chemical that is most detected by each sensor in each month can be identified.

Figure 2.1

For the month of April, the chemical that is most detected by Sensors 3, 4, 5, 6, 8 and 9 is AGOC-3A. On the contrary, for Sensor 1 the chemical that is most detected is Chlorodinine, whereas for Sensor 2 and 7, the chemical that is most detected is Methylosmolene (Figure 2.1).

Figure 2.2

For the month of August, the chemical that is most detected by Sensors 2, 3, 4, 5, 6 and 7 are AGOC-3A. However, for Sensor 1 and Sensor 8, the chemical that is most detected is Chlorodinine and Appluimonia, respectively (Figure 2.2).

Figure 2.3

For the month of December, for Sensors 1, 2, 3, 4, 5, 6 and 9, the most detected chemical is AGOC-3A. On the other hand, for Sensors 7 and 8, the most detected chemical is Methylosmolene (Figure 2.3).

Overall, across all the months and sensors, the chemical that is most detected is AGOC-3A, followed by Methylosmolene, Chlorodinine and finally Appluimonia.

To find the patterns in chemical release, the sum of readings of each chemical is plotted against hour, using color to distinguish each month.

Figure 2.4

For AGOC-3A, the readings follow a similar trend over the three months. The readings are generally higher during the period between 0600 and 2200 (Figure 2.4).

Figure 2.5

For Appluimonia and Chlorodinine, the readings follow a similar trend between each month. For both chemicals, the readings generally increase over the months, having the highest readings in December.

Figure 2.6

For Methylosmolene, the readings follow a similar trend over the three months as well. The readings are generally higher during the period between 2200 and 0600 (Figure 2.6). This is the opposite of the pattern found in the readings for AGOC-3A, which indicates a possible relationship between the release of these two chemicals.

Problem 3: Factory Responsible for Chemical Release

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. Limit your response to no more than 8 images and 1000 words.

By plotting the readings from every Sensory for every Day and Hour, filtered by the Chemical and Month, on a heatmap, the instances whereby a chemical is highly detected by a sensor can be identified. This instance can then we used to find the wind direction towards that sensor on that date and time, to find out the factory that was responsible for the release of that chemical.

Figure 3.1

Figure 3.2

For AGOC-3A, one of the instances whereby the reading for this chemical was high is on Day 13 of August at 0900 hours by Sensor 3 (Figure 3.1). On this date and time, the wind direction is such that the chemical should have been released by factories Kasios Office Furniture and Roadrunner Fitness Electronics (Figure 3.2).

Figure 3.3

Figure 3.4

For Appluimonia, one of the instances whereby the reading for this chemical was high is on Day 5 of December at 1200 hours by Sensor 9 (Figure 3.3). On this date and time, the wind direction is such that the chemical should have been released by Indigo Sol Boards (Figure 3.4).

Figure 3.5

Figure 3.6

For Chlorodinine, one of the instances whereby the reading for this chemical was high is on Day 27 of April at 0000 hours by Sensor 6 (Figure 3.5). On this date and time, the wind direction is such that the chemical should have been released by Roadrunner Fitness Electronics (Figure 3.6).

Figure 3.7

Figure 3.8

For Methylosmolene, one of the instances whereby the reading for this chemical was high is on Day 2 of December at 0300 hours by Sensor 6 (Figure 3.7). On this date and time, the wind direction is such that the chemical should have been released by Kasios Office Furniture (Figure 3.8).

Interactive Visualization

Link: https://public.tableau.com/profile/felicia.anto#!/vizhome/VAAssignment/Story1?publish=yes

Credits

Has Nilofar & Foo Ru Jia