Difference between revisions of "ISSS608 2016-17 T3 Assign MANISH MITTAL"

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==Q1==
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<div style=background:#0b3d53 border:#A3BFB1>
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Q: 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?
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[[Image:Post.png|300px]]
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<font size = 5; color="#FFFFFF">VAST Challenge: Mini Challenge 2</font>   
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</div>
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[[ISSS608_2016-17_T3_Assign_MANISH MITTAL_Overview| <font color="#FFFFFF">Introduction</font>]]
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[[ISSS608_2016-17_T3_Assign_MANISH MITTAL_DataPrep| <font color="#FFFFFF">Data Preparation</font>]]
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[[ISSS608_2016-17_T3_Assign_MANISH MITTAL_Visualizations| <font color="#FFFFFF">Insight & Conclusion</font>]]
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[[ISSS608_2016-17_T3_Assign_MANISH MITTAL_Feedback| <font color="#FFFFFF">Feedback and Comments</font>]]
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[[File:Calendar.png|950px|thumbnail|Number of records/day]]
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<!--MAIN HEADER-->
  
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By looking at the calendar plot we can see at some specific dates of April, August and December the number of readings recorded are less than usual. There are 3 dates in August for which the number of records are less 2nd, 4th & 7th. One thing to be noted by this graph is that on 2nd on every month the number of recordings are less. We further will dig down to find out the reason.
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<div style=background:#0b3d53 border:#A3BFB1>
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<font size = 3; color="#FFFFFF">Overview</font>   
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</div>
  
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[[File:Records1.png|500px|thumbnail|left]]
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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.
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[[File:Records2.png|500px|thumbnail|left]]
 
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[[File:Records3.png|500px|thumbnail|left]]
 
  
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We clearly see an unusual pattern of number of records recorded by monitors 3, 4, 5, 6 and 9. The Above screenshots shows that the unusual pattern was mainly due to the chemical AGOC – 3A. The monitors are taking multiple reading where they are not supposed to be. Looking the graphs whenever there are higher number of records noted by monitors for chemical AGOC -3A, there are lower number of records recorded for chemical Methylosmolene which means monitors are confusing the AGOC – 3A chemical with Methylosmolene. This unusual pattern is shown throughout the year for all months. This can be verified by looking at the number of records for other two chemicals Appluimonia & Chlorodinine. For these two the number of records are almost constant throughout the year.
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<div style=background:#0b3d53 border:#A3BFB1>
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<br/><br/> <br/><br/> <br/><br/> <br/><br/><br/><br/> <br/><br/><br/><br/><br/><br/> <br/><br/> <br/><br/> <br/><br/><br/><br/> <br/><br/><br/><br/><br/><br/> <br/><br/> <br/><br/> <br/><br/>
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<font size = 3; color="#FFFFFF">Mini-Challenge 2</font>    
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This unusual pattern was shown for monitors 7 & 8 also for the month of April which later got fixed in August & December.
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</div>
  
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[[File:1.png|1500px|thumbnail|center]]
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Ornithology student 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.
  
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Monitor 3 & 6 shows unusual pattern in readings as well. For all the 3 months, monitors 3 & 6 are showing extremely high readings. The same pattern was observed for monitor 4 as well but for the month of December only. We can conclude that In the month of December the wind direction was towards north west where the monitor 4 exists and hence the reading was more in monitor 4 specifically.  
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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.
  
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[[File:2.png|1500px|thumbnail|center]]
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Mini-Challenge 2 provides a three month set of data for you to analyze, covering April, August, and December 2016.
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[[File:3.png|500px|thumbnail|center]]
 
  
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==Q2==
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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. In addition, prepare a video that shows how you used visual analytics to solve this challenge. Novel visualizations and analysis approaches are especially interesting for this mini-challenge. Please do not use any other data in your work (including other Internet-based sources or other mini-challenge data).
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Which chemicals are being detected by the sensor group? What patterns of chemical releases do you see, as being reported in the data?
 
  
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[[File:Distribution of Readings.png|1000px|thumbnail|left]] 
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You may use tools you developed in other VAST Challenges in your efforts – please let us know when you do so!
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Please visit [http://vacommunity.org/VAST+Challenge+2017+MC2 VAST Challenge 2017: Mini-Challenge 2].
  
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The graphs on the left hand side show the distribution of readings by different monitors for different chemicals.
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<div style=background:#0b3d53 border:#A3BFB1>
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The few observations are -
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<font size = 3; color="#FFFFFF">Questions</font>   
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1. Sensors 3 & 6 contribute to the high number of readings in the month of April for all the chemicals which is followed by 7 & then 8th sensor.
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</div>
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2. For the month of August, sensors 3 & 4 recorded the maximum readings for all 4 chemicals, which is further followed by sensor 6.
 
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3. For the month of December, Sensor 4 shows the highest readings for all the 4 chemicals which is followed by sensor 3 & sensor 6.
 
  
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In most of the cases, sensors 3 & 4 contribute to the maximum readings which is due to the location of sensors and the wind direction. We further dig down to figure out the different patters.
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*  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.
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*  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.
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*  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.
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==Q3==
 
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Q: 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.
 
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To answer this question, the dashboard can be used. The release of chemicals can be traced by looking at the spikes in the reading of different monitors. Line graphs are plotted based on the days, months & hours for the monitors are the recorded readings. The polygons are created to show the direction of wind which further are used to observe of they are overlapping any factory coordinates.
 
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This overlapping shows a possible indication that which factory is responsible for releasing which chemical. The angle parameter is used to adjust the spread of the wind in certain ambiguous situations which will be further elaborated in the later part. Latitude & Longitude are plotted based on the angle calculated. Crosses show the position of sensors & rectangles show the positions of factories.The map used is given below -
 
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[[File:Sens&Mons.png|1000px|thumbnail|left]]
 
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[[File:Q3-1.png|1000px|thumbnail|left]]
 
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<br/><br/> <br/>
 
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On 15th April at 12PM, we can clearly see there is a spike in monitor 6. The average reading it gives is 45.51 which is quite high as compare to other days. The polygons created to show the wind direction for monitor 6 overlaps the INDIGO factory. We can say AGOC is coming out from INDIGO factory.
 
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[[File:Q3-2.png|1000px|thumbnail|left]]
 
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<br/><br/> <br/><br/> <br/><br/> <br/><br/><br/><br/> <br/><br/><br/><br/><br/><br/> <br/><br/> <br/><br/> <br/><br/> <br/>
 
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On 29th April at 9PM, we can clearly see there is a spike in monitor 6. The average reading it gives is 8.91 which is quite high for chemical Appluimonia. The polygons created to show the wind direction for monitor 6 overlaps the ROADRUNNER & KASIO factory. We can say Appluimonia is coming out from ROADRUNNER & KASIO factory.
 
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[[File:Q3-3.png|1000px|thumbnail|left]]
 
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<br/><br/> <br/><br/> <br/><br/> <br/><br/><br/><br/> <br/><br/><br/><br/><br/><br/> <br/><br/> <br/><br/> <br/>
 
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On 2nd April at 3PM, we can clearly see there is a spike in monitor 6. The average reading it gives is 54.94 which is quite high for chemical METHYLOSMOLENE. The polygons created to show the wind direction for monitor 6 overlaps the ROADRUNNER & KASIO factory. We can say METHYLOSMOLENE is coming out from ROADRUNNER & KASIO factory.
 
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[[File:Q3-4.png|1000px|thumbnail|left]]
 
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<br/><br/> <br/><br/> <br/><br/> <br/><br/><br/><br/> <br/><br/><br/><br/><br/><br/> <br/><br/> <br/><br/> <br/>
 
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On 13th August at 9PM, we can clearly see there is a spike in monitor 3. The average reading it gives is 47.30 which is quite high for chemical AGOC – 3A. The polygons created to show the wind direction for monitor 6 overlaps the KASIO factory. We can say AGOC – 3A is coming out from KASIO factory.
 
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FOR Month DECEMBER: Chemical AGOC – 3A
 
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We could see the clear graph for December month as well. The spikes are coming for monitor 4 and due to wind direction it is covering the factories Roadrunner & Kasio.
 
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[[File:Q3-5.png|1000px|thumbnail|left]]
 
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On 5th December at 12PM, we can clearly see there is a spike in monitor 9. The average reading it gives is 8.517 which is quite high for chemical Appluimonia. The polygons created to show the wind direction for monitor 6 overlaps the INDIGO factory. We can say Appluimonia is coming out from INDIGO factory.
 
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[[File:Q3-6.png|1000px|thumbnail|left]]
 
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On 11th December at 9PM, we can clearly see there is a spike in monitor 5. The average reading it gives is 9 for chemical CHLORODININE. The polygons created to show the wind direction for monitor 6 overlaps the INDIGO factory. We can say CHLORODININE is coming out mostly from Roadrunner factory.
 
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Latest revision as of 11:16, 16 July 2017

Post.png VAST Challenge: Mini Challenge 2

Introduction

Data Preparation

Insight & Conclusion

Feedback and Comments

 


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.

Mini-Challenge 2

Ornithology student 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.

Mini-Challenge 2 provides a three month set of data for you to analyze, covering April, August, and December 2016.

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. In addition, prepare a video that shows how you used visual analytics to solve this challenge. Novel visualizations and analysis approaches are especially interesting for this mini-challenge. Please do not use any other data in your work (including other Internet-based sources or other mini-challenge data).

You may use tools you developed in other VAST Challenges in your efforts – please let us know when you do so! Please visit VAST Challenge 2017: Mini-Challenge 2.

Questions

  • 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.
  • 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.
  • 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.
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